Integrating Keyword Advertising and Dynamic Pricing for an Online Market Place

Keyword Advertising is a main marketing instrument for e-commerce companies in order to generate traffic from search engines on their website. The costs for Keyword Advertising are determined in an auction that is conducted for every single search query, which is entered in by a user. In case of an online market place, each adlink provided by the search engine refers to an ordered list of products on the website of the online market place. Hereby, the price of the product is oftentimes one important criteria for the user when deciding for one or the other product from the list. However, existing models assume the price of products to be exogeneous. By taking into account the prices of linke…

Shaping communities of local optima by perturbation strength

Recent work discovered that fitness landscapes induced by Iterated Local Search (ILS) may consist of multiple clusters, denoted as funnels or communities of local optima. Such studies exist only for perturbation operators (kicks) with low strength. We examine how different strengths of the ILS perturbation operator affect the number and size of clusters. We present an empirical study based on local optima networks from NK fitness landscapes. Our results show that a properly selected perturbation strength can help overcome the effect of ILS getting trapped in clusters of local optima. This has implications for designing effective ILS approaches in practice, where traditionally only small per…

Communities of Local Optima as Funnels in Fitness Landscapes

We conduct an analysis of local optima networks extracted from fitness landscapes of the Kauffman NK model under iterated local search. Applying the Markov Cluster Algorithm for community detection to the local optima networks, we find that the landscapes consist of multiple clusters. This result complements recent findings in the literature that landscapes often decompose into multiple funnels, which increases their difficulty for iterated local search. Our results suggest that the number of clusters as well as the size of the cluster in which the global optimum is located are correlated to the search difficulty of landscapes. We conclude that clusters found by community detection in local…

Orientation matters

The optimal communication spanning tree (OCST) problem is a well known $\mathcal{NP}$-hard combinatorial optimization problem which seeks a spanning tree that satisfies all given communication requirements for minimal total costs. It has been shown that optimal solutions of OCST problems are biased towards the much simpler minimum spanning tree (MST) problem. Therefore, problem-specific representations for EAs like heuristic variants of edge-sets that are biased towards MSTs show high performance.In this paper, additional properties of optimal solutions for Euclidean variants of OCST problems are studied. Experimental results show that not only edges in optimal trees are biased towards low-…

Biased Modern Heuristics for the OCST Problem

Biasing modern heuristics is an appropriate possibility in designing problem-specific and high-quality modern heuristics. If we have knowledge about a problem we can bias the design elements of modern heuristics, namely the representation and search operator, fitness function, the initial solution, or even the search strategy. This chapter presents a case study on how the performance of modern heuristics can be increased by biasing the design elements towards high-quality solutions. Results show that problem-specific and biased modern heuristics outperform standard variants and even for large problem instances high-quality solutions can be found.

High Locality Representations for Automated Programming

We study the locality of the genotype-phenotype mapping used in grammatical evolution (GE). GE is a variant of genetic programming that can evolve complete programs in an arbitrary language using a variable-length binary string. In contrast to standard GP, which applies search operators directly to phenotypes, GE uses an additional mapping and applies search operators to binary genotypes. Therefore, there is a large semantic gap between genotypes (binary strings) and phenotypes (programs or expressions). The case study shows that the mapping used in GE has low locality leading to low performance of standard mutation operators. The study at hand is an example of how basic design principles o…

Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression Problems

Epsilon-lexicase selection is a parent selection method in genetic programming that has been successfully applied to symbolic regression problems. Recently, the combination of random subsampling with lexicase selection significantly improved performance in other genetic programming domains such as program synthesis. However, the influence of subsampling on the solution quality of real-world symbolic regression problems has not yet been studied. In this paper, we propose down-sampled epsilon-lexicase selection which combines epsilon-lexicase selection with random subsampling to improve the performance in the domain of symbolic regression. Therefore, we compare down-sampled epsilon-lexicase w…

How Smart Cities Explore New Technologies

The concept of smart city is considered as a new paradigm of urban development. Information and communication technologies are expected to transform cities into smart cities and improve the citizens’ quality of life. However, smart city initiatives still have difficulties to leverage value from technology opportunities. How smart city initiatives examine the possibilities of new technologies is therefore a highly interesting question. Based on a multiple case study we identify two different approaches and factors that influence the choice of approach: Cities either initially focus on use cases solving urban challenges, or on a systematic build-up of a technological platform for future use c…

Analysis and design of sequencing rules for car sequencing

Abstract This paper presents novel approaches for generating sequencing rules for the car sequencing (CS) problem in cases of two and multiple processing times per station. The CS problem decides on the succession of different car models launched down a mixed-model assembly line. It aims to avoid work overloads at the stations of the line by applying so-called sequencing rules, which restrict the maximum occurrence of labor-intensive options in a subsequence of a certain length. Thus to successfully avoid work overloads, suitable sequencing rules are essential. The paper shows that the only existing rule generation approach leads to sequencing rules which misclassify feasible sequences. We …

Explaining the internationalization of ibusiness firms

Information and communication technologies have given rise to a new type of firm, the ibusiness firm. These firms offer a platform that allows users to interact with each other and generate value through user co-creation of content. Because of this, ibusiness firms face different challenges when they internationalize compared with traditional firms, even those online. In this article we extend existing internationalization theory to encompass this new type of organization. We theorize that because ibusiness firms produce value through the creation and coordination of a network of users, these firms tend to suffer greater liabilities of outsidership when expanding abroad and therefore concen…

You Perceive What You Believe: The Impact of Psychological Beliefs on Perceived Technostress

An airline connection builder using maximum connection lag with greedy parameter selection

Abstract This paper introduces a methodology for designing an airline connection builder (CB) and adjusting its parameter settings. The objective of the proposed CB is to construct relevant connections that attract passenger demand while avoiding operationally infeasible and commercially irrelevant connections. Using worldwide MIDT booking data, we examined the sensitivity of CB results to the setting of the standard CB parameters maximum connection time and geographical detour. We demonstrated that CB performance can be increased by replacing these two parameters with connection lag, a measure that combines the impact of connection time with geographical detour on the total travel time of …

Inferring Decision Strategies from Clickstreams in Decision Support Systems: A New Process-Tracing Approach using State Machines

The importance of online shopping has grown remarkably over the last decade. In 2009, every West European spent on average € 483 online and this amount is expected to grow to € 601 in 2014. In Germany, the number of online shoppers has almost doubled since 2000: 44% of all adults regularly buy products onlinetoday. In Western Europe, online sales reached € 68 billion in 2009 and Forrester research forecasts it will reach € 114 billion by 2014 with an 11% compound annual growth rate.

Simultaneous Airline Scheduling

Currently, there are no solution approaches available to construct and optimize airline schedules within a single model. All existing approaches decompose the problem into smaller and less complex subproblems and solve those subproblems separately. This chapter presents a metaheuristic for simultaneous airline scheduling where several different subproblems are integrated into one single optimization model, except for crew scheduling. The problem-specific metaheuristic uses an adaptive procedure for operator selection to allow an efficient choice between a variety of different operators. Experiments are conducted as proof-of-concept and to calibrate free parameters. Comparing different searc…

On the Locality of Standard Search Operators in Grammatical Evolution

Offspring should be similar to their parents and inherit their relevant properties. This general design principle of search operators in evolutionary algorithms is either known as locality or geometry of search operators, respectively. It takes a geometric perspective on search operators and suggests that the distance between an offspring and its parents should be less than or equal to the distance between both parents. This paper examines the locality of standard search operators used in grammatical evolution (GE) and genetic programming (GP) for binary tree problems. Both standard GE and GP search operators suffer from low locality since a substantial number of search steps result in an o…

On the influence of context-based complexity on information search patterns: An individual perspective

Although context-based complexity measured as the similarity and conflict across alternatives is dependent on individual preference structures, existing studies investigating the influence of context-based complexity on information search patterns have largely ignored that context-based complexity is user- and preference-dependent. Addressing this research gap, this article elicits the individual preferences of decision makers by using the pairwise-comparison-based preference measurement (PCPM) technique and records individuals' search patterns using eye tracking. Our results show that an increased context-based complexity leads to an increase in information acquisition and the use of a mor…

Design of Representations and Search Operators

Successful and efficient use of evolutionary algorithms depends on the choice of genotypes and the representation – that is, the mapping from genotype to phenotype – and on the choice of search operators that are applied to the genotypes. These choices cannot be made independently of each other. This chapter gives recommendations on the design of representations and corresponding search operators and discusses how to consider problem-specific knowledge. For most problems in the real world, similar solutions have similar fitness values. This fact can be exploited by evolutionary algorithms if they ensure that the representations and search operators used are defined in such a way that simila…

Individual Rank and Response: Survey-Based Evidence on the Effects of Rank-Based Performance Feedback

An analysis of the bias of variation operators of estimation of distribution programming

Estimation of distribution programming (EDP) replaces standard GP variation operators with sampling from a learned probability model. To ensure a minimum amount of variation in a population, EDP adds random noise to the probabilities of random variables. This paper studies the bias of EDP's variation operator by performing random walks. The results indicate that the complexity of the EDP model is high since the model is overfitting the parent solutions when no additional noise is being used. Adding only a low amount of noise leads to a strong bias towards small trees. The bias gets stronger with an increased amount of noise. Our findings do not support the hypothesis that sampling drift is …

The Car Resequencing Problem with Pull-Off Tables

AbstractThe car sequencing problem determines sequences of different car models launched down a mixed- model assembly line. To avoid work overloads of workforce, car sequencing restricts the maximum occurrence of labor-intensive options, e.g., a sunroof, by applying sequencing rules. We consider this problem in a resequencing context, where a given number of buffers (denoted as pull-off tables) is available for rearranging a stirred sequence. The problem is formalized and suited solution procedures are developed. A lower bound and a dominance rule are introduced which both reduce the running time of our graph approach. Finally, a real-world resequencing setting is investigated.

Predicting Heuristic Search Performance with PageRank Centrality in Local Optima Networks

Previous studies have used statistical analysis of fitness landscapes such as ruggedness and deceptiveness in order to predict the expected quality of heuristic search methods. Novel approaches for predicting the performance of heuristic search are based on the analysis of local optima networks (LONs). A LON is a compressed stochastic model of a fitness landscape's basin transitions. Recent literature has suggested using various LON network measurements as predictors for local search performance.In this study, we suggest PageRank centrality as a new measure for predicting the performance of heuristic search methods using local search. PageRank centrality is a variant of Eigenvector centrali…

Decomposition of Dynamic Single-Product and Multi-product Lotsizing Problems and Scalability of EDAs

In existing theoretical and experimental work, Estimation of Distribution Algorithms (EDAs) are primarily applied to decomposable test problems. State-of-the-art EDAs like the Hierarchical Bayesian Optimization Algorithm (hBOA), the Learning Factorized Distribution Algorithm (LFDA) or Estimation of Bayesian Networks Algorithm (EBNA) solve these problems in polynomial time. Regarding this success, it is tempting to apply EDAs to real-world problems. But up to now, it has rarely been analyzed which real-world problems are decomposable. The main contribution of this chapter is twofold: (1) It shows that uncapacitated single-product and multi-product lotsizing problems are decomposable. (2) A s…

An Encoding in Metaheuristics for the Minimum Communication Spanning Tree Problem

Problem-specific encodings can improve the performance of metaheuristics, such as genetic algorithms or simulated annealing. This paper studies the link-biased (LB) encoding, which is a tree representation, and applies metaheuristics using this encoding to the minimum communication spanning tree (MCST) problem. Given the communication requirements of the nodes, the MCST problem seeks a communication spanning tree with minimum total cost. Optimal solutions for MCST problems are similar to minimum spanning trees (MSTs), and the LB encoding exploits this property by encoding trees similar to MSTs with higher probability. The paper investigates how to systematically design problem-specific enc…

Analysis of properties of recombination operators proposed for the node-depth encoding

The node-depth encoding is a representation for evolutionary algorithms applied to tree problems. Its represents trees by storing the nodes and their depth in a proper ordered list. The original formulation of the node-depth encoding has only mutation operators as the search mechanism. Although it is computationally efficient, the exclusive use of mutation restricts the exploration of the search space and the algorithm convergence. Then, this work proposes two specific recombination operators to improve the convergence of the algorithm using the node-depth encoding representation. These operators are based on recombination operators for permutation representations. Analysis of the proposed …

Reference point based multi-objective evolutionary algorithms for group decisions

While in the past decades research on multi-objective evolutionary algorithms (MOEA) has aimed at finding the whole set of Pareto optimal solutions, current approaches focus on only those parts of the Pareto front which satisfy the preferences of the decision maker (DM). Therefore, they integrate the DM early on in the optimization process instead of leaving him/her alone with the final choice of one solution among the whole Pareto optimal set. In this paper, we address an aspect which has been neglected so far in the research on integrating preferences: in most real-world problems, there is not only one DM, but a group of DMs trying to find one consensus decision all participants are wille…

On the role of non-effective code in linear genetic programming

In linear variants of Genetic Programming (GP) like linear genetic programming (LGP), structural introns can emerge, which are nodes that are not connected to the final output and do not contribute to the output of a program. There are claims that such non-effective code is beneficial for search, as it can store relevant and important evolved information that can be reactivated in later search phases. Furthermore, introns can increase diversity, which leads to higher GP performance. This paper studies the role of non-effective code by comparing the performance of LGP variants that deal differently with non-effective code for standard symbolic regression problems. As we find no decrease in p…

A generalizability measure for program synthesis with genetic programming

The generalizability of programs synthesized by genetic programming (GP) to unseen test cases is one of the main challenges of GP-based program synthesis. Recent work showed that increasing the amount of training data improves the generalizability of the programs synthesized by GP. However, generating training data is usually an expensive task as the output value for every training case must be calculated manually by the user. Therefore, this work suggests an approximation of the expected generalization ability of solution candidates found by GP. To obtain candidate solutions that all solve the training cases, but are structurally different, a GP run is not stopped after the first solution …

Edge Orientation and the Design of Problem-Specific Crossover Operators for the OCST Problem

In the Euclidean optimal communication spanning tree problem, the edges in optimal trees not only have small weights but also point with high probability toward the center of the graph. These characteristics of optimal solutions can be used for the design of problem-specific evolutionary algorithms (EAs). Recombination operators of direct encodings like edge-set and NetDir can be extended such that they prefer not only edges with small distance weights but also edges that point toward the center of the graph. Experimental results show higher performance and robustness in comparison to EAs using existing crossover strategies.

Motivating Low-Achievers—Relative Performance Feedback in Primary Schools

Abstract Relative performance feedback (RPF) has often been shown to improve effort and performance in the workplace and educational settings. Yet, many studies also document substantial negative effects of RPF, in particular for low-achievers. We study a novel type of RPF designed to overcome these negative effects of RPF on low-achievers by scoring individual performance improvements. With a sample of 400 children, we conduct a class-wise randomized-controlled trial using an e-learning software in regular teaching lessons in primary schools. We demonstrate that this type of RPF significantly increases motivation, effort, and performance in math for low-achieving children, without hurting …

On Optimal Solutions for the Optimal Communication Spanning Tree Problem

This paper presents an experimental investigation into the properties of the optimal communication spanning tree (OCST) problem. The OCST problem seeks a spanning tree that connects all the nodes and satisfies their communication requirements at a minimum total cost. The paper compares the properties of random trees to the properties of the best solutions for the OCST problem that are found using an evolutionary algorithm. The results show, on average, that the optimal solution and the minimum spanning tree (MST) share a higher number of links than the optimal solution and a random tree. Furthermore, optimal solutions for OCST problems with randomly chosen distance weights share a higher n…

An Analysis of the Influence of Noneffective Instructions in Linear Genetic Programming

Abstract Linear Genetic Programming (LGP) represents programs as sequences of instructions and has a Directed Acyclic Graph (DAG) dataflow. The results of instructions are stored in registers that can be used as arguments by other instructions. Instructions that are disconnected from the main part of the program are called noneffective instructions, or structural introns. They also appear in other DAG-based GP approaches like Cartesian Genetic Programming (CGP). This article studies four hypotheses on the role of structural introns: noneffective instructions (1) serve as evolutionary memory, where evolved information is stored and later used in search, (2) preserve population diversity, (3)…

Improving estimation of distribution genetic programming with novelty initialization

Estimation of distribution genetic programming (EDA-GP) replaces the standard variation operations of genetic programming (GP) by learning and sampling from a probabilistic model. Unfortunately, many EDA-GP approaches suffer from a rapidly decreasing population diversity which often leads to premature convergence. However, novelty search, an approach that searches for novel solutions to cover sparse areas of the search space, can be used for generating diverse initial populations. In this work, we propose novelty initialization and test this new method on a generalization of the royal tree problem and compare its performance to ramped half-and-half (RHH) using a recent EDA-GP approach. We f…

Code-share connectivity within global airline alliances – How much potential is utilized?

Abstract This paper analyzes the code-share connectivity of carriers from the three global alliances: Star Alliance, Sky Team and oneworld. We generate 2-leg online and code-share connections to evaluate the existing connectivity. Additionally, we generate all potential interline connections between members of the same alliance that are not yet supported with existing code-shares and analyze what share of the potential connectivity remains unused. We find that code-share connections account to about one-fourth of the total number of international connections offered by alliance members. 73% of those code-share connections are with partners from the same alliance, 6% with carriers from compe…

Using penalties instead of rewards: Solving OCST problems with guided local search

Abstract This paper considers the optimal communication spanning tree (OCST) problem. Previous work analyzed features of high-quality solutions and found that edges in optimal solutions have low weight and point towards the center of a tree. Consequently, integrating this problem-specific knowledge into a metaheuristic increases its performance for the OCST problem. In this paper, we present a guided local search (GLS) approach which dynamically changes the objective function to guide the search process into promising areas. In contrast to traditional approaches which reward promising solution features by favoring edges with low weights pointing towards the tree’s center, GLS penalizes low-…

Using knowledge of human-generated code to bias the search in program synthesis with grammatical evolution

Recent studies show that program synthesis with GE produces code that has different structure compared to human-generated code, e.g., loops and conditions are hardly used. In this article, we extract knowledge from human-generated code to guide evolutionary search. We use a large code-corpus that was mined from the open software repository service GitHub and measure software metrics and properties describing the code-base. We use this knowledge to guide the search by incorporating a new selection scheme. Our new selection scheme favors programs that are structurally similar to the programs in the GitHub code-base. We find noticeable evidence that software metrics can help in guiding evoluti…

On the Role of Social Comparison Processes in Gamified Work Situations

The node-depth encoding

The node-depth encoding has elements from direct and indirect encoding for trees which encodes trees by storing the depth of nodes in a list. Node-depth encoding applies specific search operators that is a typical characteristic for direct encodings. An investigation into the bias of the initialization process and the mutation operators of the node-depth encoding shows that the initialization process has a bias to solutions with small depths and diameters, and a bias towards stars. This investigation, also, shows that the mutation operators are unbiased. The performance of node-depth encoding is investigated for the bounded-diameter minimum spanning tree problem. The results are presented f…

CovSel

Ensemble methods combine the predictions of a set of models to reach a better prediction quality compared to a single model's prediction. The ensemble process consists of three steps: 1) the generation phase where the models are created, 2) the selection phase where a set of possible ensembles is composed and one is selected by a selection method, 3) the fusion phase where the individual models' predictions of the selected ensemble are combined to an ensemble's estimate. This paper proposes CovSel, a selection approach for regression problems that ranks ensembles based on the coverage of adequately estimated training points and selects the ensemble with the highest coverage to be used in th…

Structural difficulty in grammatical evolution versus genetic programming

Genetic programming (GP) has problems with structural difficulty as it is unable to search effectively for solutions requiring very full or very narrow trees. As a result of structural difficulty, GP has a bias towards narrow trees which means it searches effectively for solutions requiring narrow trees. This paper focuses on the structural difficulty of grammatical evolution (GE). In contrast to GP, GE works on variable-length binary strings and uses a grammar in Backus-Naur Form (BNF) to map linear genotypes to phenotype trees. The paper studies whether and how GE is affected by structural difficulty. For the analysis, we perform random walks through the search space and compare the struc…

Teaching GP to program like a human software developer

Program synthesis is one of the relevant applications of GP with a strong impact on new fields such as genetic improvement. In order for synthesized code to be used in real-world software, the structure of the programs created by GP must be maintainable. We can teach GP how real-world software is built by learning the relevant properties of mined human-coded software - which can be easily accessed through repository hosting services such as GitHub. So combining program synthesis and repository mining is a logical step. In this paper, we analyze if GP can write programs with properties similar to code produced by human software developers. First, we compare the structure of functions generat…

How to start with big data - a multiple case study

A genetic algorithm for analyzing choice behavior with mixed decision strategies

In the field of decision-making a fundamental problem is how to uncover people's choice behavior. While choices them- selves are often observable, our underlying decision strategies determining these choices are not entirely understood. Previous research defined a number of decision strategies and conjectured that people do not apply only one strategy but switch strategies during the decision process. To the best of our knowledge, empirical evidence for the latter conjecture is missing. This is why we monitored the purchase decisions 624 consumers shopping online. We study how many of the observed choices can be explained by the existing strategies in their pure form, how many decisions can…

DAE-GP

Estimation of distribution genetic programming (EDA-GP) algorithms are metaheuristics where sampling new solutions from a learned probabilistic model replaces the standard mutation and recombination operators of genetic programming (GP). This paper presents DAE-GP, a new EDA-GP which uses denoising autoencoder long short-term memory networks (DAE-LSTMs) as probabilistic model. DAE-LSTMs are artificial neural networks that first learn the properties of a parent population by mapping promising candidate solutions to a latent space and reconstructing the candidate solutions from the latent space. The trained model is then used to sample new offspring solutions. We show on a generalization of t…

The crowd against the few: Measuring the impact of expert recommendations

Abstract A large amount of research on recommender systems has focused on improving the accuracy of suggestions in offline settings. However, this focus and the commonly used techniques can lead to a “filter bubble”, severely limiting the diversity of content discovered by users. Several offline studies show that this can be mitigated by using experts for recommendation. In contrast to standard recommender systems, experts are able to generate more diverse recommendations and increase the novelty of given suggestions. They can be used in missing-data or cold-start scenarios and reduce noise in the users' ratings. This paper examines the impact of employed experts' recommendations on user be…

Representations for evolutionary algorithms

Successful and efficient use of evolutionary algorithms (EA) depends on the choice of the genotype, the problem representation (mapping from genotype to phenotype) and on the choice of search operators that are applied to the genotypes. These choices cannot be made independently of each other. The question whether a certain representation leads to better performing EAs than an alternative representation can only be answered when the operators applied are taken into consideration. The reverse is also true: deciding between alternative operators is only meaningful for a given representation. In EA practice one can distinguish two complementary approaches. The first approach uses indirect repr…

An Optimized Design of Choice Experiments: A New Approach for Studying Decision Behavior in Choice Task Experiments

In this paper, we present a new approach for the optimal experimental design problem of generating diagnostic choice tasks, where the respondent's decision strategy can be unambiguously deduced from the observed choice. In this new approach, we applied a genetic algorithm that creates a one-to-one correspondence between a set of predefined decision strategies and the alternatives of the choice task; it also manipulates the characteristics of the choice tasks. In addition, this new approach takes into account the measurement errors that can occur when the preferences of the decision makers are being measured. The proposed genetic algorithm is capable of generating diagnostic choice tasks eve…

Using PageRank for non-personalized default rankings in dynamic markets

Abstract Default ranking algorithms are used to generate non-personalized product rankings for standard consumers, for example, on landing pages of online stores. Default rankings are created without any information about the consumers’ preferences. This paper proposes using the product centrality ranking algorithm (PCRA), which solves some problems of existing default ranking algorithms: Existing approaches either have low accuracy, because they rely on only one product attribute, or they are unable to estimate ranks for new or updated products, because they use past consumer behavior, such as previous sales or ratings. The PCRA uses the PageRank centrality of products in a product dominat…

On the Bias of Syntactic Geometric Recombination in Genetic Programming and Grammatical Evolution

For fixed-length binary representations as used in genetic algorithms, standard recombination operators (e.g.,~one-point crossover) are unbiased. Thus, the application of recombination only reshuffles the alleles and does not change the statistical properties in the population. Using a geometric view on recombination operators, most search operators for fixed-length strings are geometric, which means that the distances between offspring and their parents are less than, or equal to, the distance between their parents. In genetic programming (GP) and grammatical evolution (GE), the situation is different since the recombination operators are applied to variable-length structures. Thus, most r…

On sampling error in evolutionary algorithms

The initial population in evolutionary algorithms (EAs) should form a representative sample of all possible solutions (the search space). While large populations accurately approximate the distribution of possible solutions, small populations tend to incorporate a sampling error. A low sampling error at initialization is necessary (but not sufficient) for a reliable search since a low sampling error reduces the overall random variations in a random sample. For this reason, we have recently presented a model to determine a minimum initial population size so that the sampling error is lower than a threshold, given a confidence level. Our model allows practitioners of, for example, genetic pro…

Challenges of Program Synthesis with Grammatical Evolution

Program synthesis is an emerging research topic in the field of EC with the potential to improve real-world software development. Grammar-guided approaches like GE are suitable for program synthesis as they can express common programming languages with their required properties. This work uses common software metrics (lines of code, McCabe metric, size and depth of the abstract syntax tree) for an analysis of GE’s search behavior and the resulting problem structure. We find that GE is not able to solve program synthesis problems, where correct solutions have higher values of the McCabe metric (which means they require conditions or loops). Since small mutations of high-quality solutions str…

Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization

Abstract Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are generative neural networks with these desired properties. We integrate an RBM into an EDA and evaluate the performance of this system in solving combinatorial optimization problems with a single objective. We assess how the number of fitness evaluations and the CPU time scale with problem size and complexity. The results are compared to the Bayesian Optimization Algorithm (BOA), a state-of-the-art multivariate EDA, and the Dependency Tree Algorithm (DTA), which uses a simpler probability model requiring less computati…

Guided local search for the optimal communication spanning tree problem

This paper considers the optimal communication spanning tree (OCST) problem. Previous work analyzed features of high-quality solutions. Consequently, integrating this knowledge into a metaheuristic increases its performance for the OCST problem. In this paper, we present a guided local search (GLS) approach which dynamically changes the objective function to guide the search process into promising areas. In contrast to traditional approaches which reward promising solution features by favoring edges with low weights pointing towards the tree's center, GLS penalizes low-quality edges with large weights that do not point towards the tree's center.

Coarse-Grained Barrier Trees of Fitness Landscapes

Recent literature suggests that local optima in fitness landscapes are clustered, which offers an explanation of why perturbation-based metaheuristics often fail to find the global optimum: they become trapped in a sub-optimal cluster. We introduce a method to extract and visualize the global organization of these clusters in form of a barrier tree. Barrier trees have been used to visualize the barriers between local optima basins in fitness landscapes. Our method computes a more coarsely grained tree to reveal the barriers between clusters of local optima. The core element is a new variant of the flooding algorithm, applicable to local optima networks, a compressed representation of fitnes…

Efficiency improvement of DC* through a Genetic Guidance

DC∗ is a method for generating interpretable fuzzy information granules from pre-classified data. It is based on the subsequent application of LVQ1 for data compression and an ad-hoc procedure based on A∗ to represent data with the minimum number of fuzzy information granules satisfying some interpretability constraints. While being efficient in tackling several problems, the A∗ procedure included in DC∗ may happen to require a long computation time because the A∗ algorithm has exponential time complexity in the worst case. In this paper, we approach the problem of driving the search process of A∗ by suggesting a close-to-optimal solution that is produced through a Genetic Algorithm (GA). E…

An implicitly parallel EDA based on restricted boltzmann machines

We present a parallel version of RBM-EDA. RBM-EDA is an Estimation of Distribution Algorithm (EDA) that models dependencies between decision variables using a Restricted Boltzmann Machine (RBM). In contrast to other EDAs, RBM-EDA mainly uses matrix-matrix multiplications for model estimation and sampling. Hence, for implementation, standard libraries for linear algebra can be used. This allows an easy parallelization and leads to a high utilization of parallel architectures. The probabilistic model of the parallel version and the version on a single core are identical. We explore the speedups gained from running RBM-EDA on a Graphics Processing Unit. For problems of bounded difficulty like …

How Companies Develop a Culture for Digital Innovation: A Multiple-Case Study

Companies are constantly facing new challenges through digital technologies, as they can cause rapid changes in their business environment. Digital innovations can offer the opportunity to gain competitive advantages, but in practice, companies often struggle to create the conditions for this. Developing a culture for digital innovation is an important capability that companies need to build. It includes appropriate organizational structures, working methods, skills, and new ways of thinking in the innovation process. This has been explored in previous research at a high level of abstraction. Our paper complements past findings with a detailed study of what activities companies are doing in…

IMPACT OF TIMETABLE SYNCHRONIZATION ON HUB CONNECTIVITY OF EUROPEAN CARRIERS

This paper evaluates the net impact of timetable synchronization on the connectivity of the key European carriers at their main hubs. We measure hub connectivity using a weighted connectivity score (WCS) that takes into account the number and the trip time related quality of flight connections. Based on WCS, we compare hub performance resulting from the existing schedule against a random expectati on calculated from multiple randomized schedule simulations. In each simulated schedule scenario we randomly vary the flight departure and arrival times within the operation hours at a hub and at outbound stations keeping all other flight parameters from the real schedule unchanged.We observe that…

Car sequencing versus mixed-model sequencing: A computational study

Abstract The paper deals with the two most important mathematical models for sequencing products on a mixed-model assembly line in order to minimize work overload the mixed-model sequencing (MMS) model and the car sequencing (CS) model. Although both models follow the same underlying objective, only MMS directly addresses the work overload in its objective function. CS instead applies a surrogate objective using so-called sequencing rules which restrict labor-intensive options accompanied with the products in the sequence. The CS model minimizes the number of violations of the respective sequencing rules, which is widely assumed to lead to minimum work overload. This paper experimentally co…

On the Effects of Relative Performance Feedback in a Risk Elicitation Task: Evidence From a Lab Experiment

This paper experimentally investigates the influence of different non-payoff-relevant feedback regimes on motivation, competitiveness and risk-taking behaviors in a risk elicitation task. We explore four feedback regimes: private feedback, full ranking feedback, top-5 ranking feedback, and non-ranking feedback based on the median performance of peers. We found that providing relative performance feedback increases risk-taking behavior, especially when using a top-5 ranking. Feedback regimes that provide a user with rank-based feedback have adverse effects on the users' attitudes as they increase competitiveness while failing to increase intrinsic motivation and performance motivation. In co…

On the Bias and Performance of the Edge-Set Encoding

The edge-set encoding of trees directly represents trees as sets of their edges. Nonheuristic operators for edge-sets manipulate trees' edges without regard for their weights, while heuristic operators consider edges' weights when including or excluding them. In the latter case, the operators generally favor edges with lower weights, and they tend to generate trees that resemble minimum spanning trees. This bias is strong, which suggests that evolutionary algorithms (EAs) that employ heuristic operators will succeed when optimum solutions resemble minimum spanning trees (MSTs) but fail otherwise. The one-max tree problem is a scalable test problem for trees where the optimum solution can be…

On the Non-uniform Redundancy of Representations for Grammatical Evolution: The Influence of Grammars

The representation used in grammatical evolution (GE) is non-uniformly redundant as some phenotypes are represented by more genotypes than others. This article studies how the non-uniform redundancy of the GE representation depends on various types of grammars. When constructing the phenotype tree from a genotype, the used grammar determines Bavg, the average branching factor. Bavg measures the expected number of non-terminals chosen when mapping one genotype codon to a phenotype tree node. First, the paper illustrates that the GE representation induces a bias towards small trees. This bias gets stronger with lower Bavg. For example, when using a grammar with Bavg = 0.5, 75% of all genotype…

New insights into the OCST problem

This paper considers the Euclidean variant of the optimal communciation spanning tree (OCST) problem. Researches have analyzed the structure of the problem and found that high quality solutions prefer edges of low cost. Further, edges pointing to the center of the network are more likely to be included in good solutions. We add to the literature and provide additional insights into the structure of the OCST problem. Therefore, we investigate properies of the whole tree, such as node degrees and the Wiener index. The results reveal that optimal solutions are structured in a star-like manner. There are few nodes with high node degrees, these nodes are located next to the graph's center. The m…