Search results for "evoluutiolaskenta"

A Feature Rich Distance-Based Many-Objective Visualisable Test Problem Generator

2019

In optimiser analysis and design it is informative to visualise how a search point/population moves through the design space over time. Visualisable distance-based many-objective optimisation problems have been developed whose design space is in two-dimensions with arbitrarily many objective dimensions. Previous work has shown how disconnected Pareto sets may be formed, how problems can be projected to and from arbitrarily many design dimensions, and how dominance resistant regions of design space may be defined. Most recently, a test suite has been proposed using distances to lines rather than points. However, active use of visualisable problems has been limited. This may be because the ty…

Parallel global optimization : structuring populations in differential evolution

2010

Evolutionary cloud for cooperative UAV coordination

2014

Diverse partner selection with brood recombination in genetic programming

2018

The ultimate goal of learning algorithms is to find the best solution from a search space without testing each and every solution available in the search space. During the evolution process new solutions (children) are produced from existing solutions (parents), where new solutions are expected to be better than existing solutions. This paper presents a new parent selection method for the crossover operation in genetic programming. The idea is to promote crossover between two behaviourally (phenotype) diverse parents such that the probability of children being better than their parents increases. The relative phenotype strengths and weaknesses of pairs of parents are exploited to find out i…

Evolutionary Algorithms and Metaheuristics : Applications in Engineering Design and Optimization

2018

Interview: Kalyanmoy Deb Talks about Formation, Development and Challenges of the EMO Community, Important Positions in His Career, and Issues Faced …

2023

Kalyanmoy Deb was born in Udaipur, Tripura, the smallest state of India at the time, in 1963 [...]

Evolutionary approach for achieving structural coverage in testing IEC 61499 function block systems

2015

The topic of this thesis is automated test generation for control software represented in a specific standard, the IEC 61499. This standard, which is largely based on the concept of function block, establishes a way to design distributed control systems in a visually clear way. The goal of the thesis was to design a test generation approach or a number of such approaches that would produce input test data with high coverage of the implementation of systems under test. Coverage is a measure which expresses the fraction of the system that was exercised at least ones when all tests in a test suite were run on this system. To reach the stated goal, evolutionary computation, a general optimizati…

Probabilistic Selection Approaches in Decomposition-based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization

2022

In offline data-driven multiobjective optimization, no new data is available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective evolutionary algorithm can be utilized to find solutions by using these surrogates. The accuracy of the approximated solutions depends on the surrogates and approximations typically involve uncertainties. In this paper, we propose probabilistic selection approaches that utilize the uncertainty information of the Kriging models (as surrogates) to improve the solution process in offline data-driven multiobjective optimization. These approaches are designed for decomposition-base…

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…

A Multiple Surrogate Assisted Decomposition Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization

2019

Many-objective optimization problems (MaOPs) contain four or more conflicting objectives to be optimized. A number of efficient decomposition-based evolutionary algorithms have been developed in the recent years to solve them. However, computationally expensive MaOPs have been scarcely investigated. Typically, surrogate-assisted methods have been used in the literature to tackle computationally expensive problems, but such studies have largely focused on problems with 1–3 objectives. In this paper, we present an approach called hybrid surrogate-assisted many-objective evolutionary algorithm to solve computationally expensive MaOPs. The key features of the approach include: 1) the use of mul…