Search results for "Evolutionary algorithm"

Applications of Evolutionary Computation

2011

EvoCOMPLEX Contributions.- Coevolutionary Dynamics of Interacting Species.- Evolving Individual Behavior in a Multi-agent Traffic Simulator.- On Modeling and Evolutionary Optimization of Nonlinearly Coupled Pedestrian Interactions.- Revising the Trade-off between the Number of Agents and Agent Intelligence.- Sexual Recombination in Self-Organizing Interaction Networks.- Symbiogenesis as a Mechanism for Building Complex Adaptive Systems: A Review.- EvoGAMES Contributions.- Co-evolution of Optimal Agents for the Alternating Offers Bargaining Game.- Fuzzy Nash-Pareto Equilibrium: Concepts and Evolutionary Detection.- An Evolutionary Approach for Solving the Rubik's Cube Incorporating Exact Met…

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

2017

Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approxim…

An Interactive Framework for Offline Data-Driven Multiobjective Optimization

2020

We propose a framework for solving offline data-driven multiobjective optimization problems in an interactive manner. No new data becomes available when solving offline problems. We fit surrogate models to the data to enable optimization, which introduces uncertainty. The framework incorporates preference information from a decision maker in two aspects to direct the solution process. Firstly, the decision maker can guide the optimization by providing preferences for objectives. Secondly, the framework features a novel technique for the decision maker to also express preferences related to maximum acceptable uncertainty in the solutions as preferred ranges of uncertainty. In this way, the d…

A New Paradigm in Interactive Evolutionary Multiobjective Optimization

2020

Over the years, scalarization functions have been used to solve multiobjective optimization problems by converting them to one or more single objective optimization problem(s). This study proposes a novel idea of solving multiobjective optimization problems in an interactive manner by using multiple scalarization functions to map vectors in the objective space to a new, so-called preference incorporated space (PIS). In this way, the original problem is converted into a new multiobjective optimization problem with typically fewer objectives in the PIS. This mapping enables a modular incorporation of decision maker’s preferences to convert any evolutionary algorithm to an interactive one, whe…

Improved usability of the minimal model of insulin sensitivity based on an automated approach and genetic algorithms for parameter estimation.

2006

Minimal model analysis of glucose and insulin data from an IVGTT (intravenous glucose tolerance test) is widely used to estimate insulin sensitivity; however, the use of the model often requires intervention by a trained operator and some problems can occur in the estimation of model parameters. In the present study, a new method for minimal model analysis, termed GAMMOD, was developed based on genetic algorithms for the estimation of model parameters. Such an algorithm does not require the fixing of initial values for the parameters (that may lead to unreliable estimates). Our method also implements an automated weighting scheme not requiring manual intervention of the operator, thus impro…

Evolutionary Algorithms and Metaheuristics : Applications in Engineering Design and Optimization

2018

Some Effects of Individual Learning on the Evolution of Sensors

2001

In this paper, we present an abstract model of sensor evolution, where sensor development is only determined by artificial evolution and the adaptation of agent reactions is accomplished by individual learning. With the environment cast into a MDP framework, sensors can be conceived as a map from environmental states to agent observations and Reinforcement Learning algorithms can be utilised. On the basis of a simple gridworld scenario, we present some results of the interaction between individual learning and evolution of sensors.

On the Locality of Standard Search Operators in Grammatical Evolution

2014

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…

Impact of chaotic dynamics on the performance of metaheuristic optimization algorithms : An experimental analysis

2022

Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin's theory of evolution as well as Mendel's theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are use…

GenClust: A genetic algorithm for clustering gene expression data

2005

Abstract Background Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. Results GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, …