Evolution strategies – A comprehensive introduction

Beyer, Hans-Georg; Schwefel, Hans–Paul

doi:10.1023/a:1015059928466

Cited by 2,115 publications

(261 citation statements)

References 60 publications

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“…In the Baldwin effect, selection may act in two ways: (1) by changing the mean trait values in the population, but without changing the level of plasticity; (2) by increasing the level of plasticity when acting on the phenotype: the individuals that are positively selected are the most plastic ones, possessing the most extreme phenotype.…”

Section: Comparing the Baldwin Effect And Genetic Assimilationmentioning

confidence: 99%

Designing and Tuning Adaptive Systems through Evolution, Learning and Meme Transmission: Biological and Computational Paradigms

Georgescu¹

2018

ECECSR

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Section: Comparing the Baldwin Effect And Genetic Assimilationmentioning

confidence: 99%

Designing and Tuning Adaptive Systems through Evolution, Learning and Meme Transmission: Biological and Computational Paradigms

Georgescu¹

2018

ECECSR

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“…the aggregated state of all composed components. The composition of components in Haiku is simply illustrated in the context of the Evolutionary Strategy (ES) metaheuristic [2]. ES is a population-based metaheuristic that has been applied across a wide range of problem domains.…”

Section: Actual Haiku Codementioning

confidence: 99%

Haiku - a Scala Combinator Toolkit for Semi-automated Composition of Metaheuristics

Kocsis

Brownlee

Swan

et al. 2015

Search-Based Software Engineering

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Abstract. There is an emerging trend towards the automated design of metaheuristics at the software component level. In principle, metaheuristics have a relatively clean decomposition, where well-known frameworks such as ILS and EA are parameterised by variant components for acceptance, perturbation etc. Automated generation of these frameworks is not so simple in practice, since the coupling between components may be implementation specific. Compositionality is the ability to freely express a space of designs 'bottom up' in terms of elementary components: previous work in this area has used combinators, a modular and functional approach to componentisation arising from foundational Computer Science. In this article, we describe Haiku, a combinator toolkit written in the Scala language, which builds upon previous work to further automate the process by automatically composing the external dependencies of components. We provide examples of use and give a case study in which a programatically-generated heuristic is applied to the Travelling Salesman Problem within an Evolutionary Strategies framework.

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“…ES is a variant of Evolutionary Algorithms (EA), which uses mutation, recombination and selection applied to a population of individuals containing candidate solutions in order to evolve iteratively better and better solutions (Beyer and Schwefel, 2002). Evolution strategies were invented by Rechenberg (1973;Schwefel, 1981) and illustrate selfadaptation of strategy parameters in evolutionary computing.…”

Section: Elitist Non-dominated Sorting Evolution Strategy (Enses)mentioning

confidence: 99%

Multi-Objective Evolutionary Optimization of Sandwich Structures: An Evaluation by Elitist Non-dominated Sorting Evolution Strategy

Akmar¹,

Krämer²,

Rabczuk³

2015

American Journal of Engineering and Applied Sciences

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Abstract:In this study, an application of evolutionary multi-objective optimization algorithms on the optimization of sandwich structures is presented. The solution strategy is known as Elitist Non-Dominated Sorting Evolution Strategy (ENSES) wherein Evolution Strategies (ES) as Evolutionary Algorithm (EA) in the elitist Non-dominated Sorting Genetic algorithm (NSGA-II) procedure. Evolutionary algorithm seems a compatible approach to resolve multi-objective optimization problems because it is inspired by natural evolution, which closely linked to Artificial Intelligence (AI) techniques and elitism has shown an important factor for improving evolutionary multi-objective search. In order to evaluate the notion of performance by ENSES, the well-known study case of sandwich structures are reconsidered. For Case 1, the goals of the multi-objective optimization are minimization of the deflection and the weight of the sandwich structures. The length, the core and skin thicknesses are the design variables of Case 1. For Case 2, the objective functions are the fabrication cost, the beam weight and the end deflection of the sandwich structures. There are four design variables i.e., the weld height, the weld length, the beam depth and the beam width in Case 2. Numerical results are presented in terms of Paretooptimal solutions for both evaluated cases.

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Evolution strategies – A comprehensive introduction

Cited by 2,115 publications

References 60 publications

Designing and Tuning Adaptive Systems through Evolution, Learning and Meme Transmission: Biological and Computational Paradigms

Designing and Tuning Adaptive Systems through Evolution, Learning and Meme Transmission: Biological and Computational Paradigms

Haiku - a Scala Combinator Toolkit for Semi-automated Composition of Metaheuristics

Multi-Objective Evolutionary Optimization of Sandwich Structures: An Evaluation by Elitist Non-dominated Sorting Evolution Strategy

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