Fitness sharing and niching methods revisited

Sareni, Bruno; Krähenbühl, Laurent

doi:10.1109/4235.735432

Cited by 481 publications

(273 citation statements)

References 9 publications

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“…Further exploration is afforded by diversity enhancing operators, such as niching (Sareni and Krähenbühl (1998), Epitropakis et al (2011)). This is particularly popular in multi-objective optimization, where good coverage of the Pareto front is needed (Horn et al (1994)).…”

Section: Nature-inspired Algorithms Such As Ga and Psomentioning

confidence: 99%

Parallel surrogate-assisted global optimization with expensive functions – a survey

Haftka

Villanueva

Chaudhuri

2016

Struct Multidisc Optim

199

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Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention.

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Section: Nature-inspired Algorithms Such As Ga and Psomentioning

confidence: 99%

Parallel surrogate-assisted global optimization with expensive functions – a survey

Haftka

Villanueva

Chaudhuri

2016

Struct Multidisc Optim

199

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“…We use the most popular version of fitness sharing (Sareni and Krähenbühl, 1998), with parameter α = 1 and different niche radius ρ. The niche radius was chosen relative to mutation range σ: ρ = (1, 5, 10) · σ.…”

Section: Impatience Vs Fitness Sharingmentioning

confidence: 99%

“…A population trapped at the optimum usually has limited diversity and limited ability to find another, possibly better, optimum. Many strategies of regaining population diversity have been proposed: crowding (DeJong, 1975;Mengshoel and Goldberg, 2008;Kowalczuk and Białaszewski, 2006), fitness sharing (Goldberg and Richardson, 1987;Sareni and Krähenbühl, 1998) and spatially structured populations (Tomassini, 2005;Dick and Whigham, 2006) are the most popular. In order to preserve diversity, hybrid methods combine different optimization strategies at various stages of the search process (Grosan and Abraham, 2007;Barabasz et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

The impatience mechanism as a diversity maintaining and saddle crossing strategy

Karcz-Dulęba

2016

International Journal of Applied Mathematics and Computer Science

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The impatience mechanism diversifies the population and facilitates escaping from a local optima trap by modifying fitness values of poorly adapted individuals. In this paper, two versions of the impatience mechanism coupled with a phenotypic model of evolution are studied. A population subordinated to a basic version of the impatience mechanism polarizes itself and evolves as a dipole centered around an averaged individual. In the modified version, the impatience mechanism is supplied with extra knowledge about a currently found optimum. In this case, the behavior of a population is quite different than previously-considerable diversification is also observed, but the population is not polarized and evolves as a single cluster. The impatience mechanism allows crossing saddles relatively fast in different configurations of bimodal and multimodal fitness functions. Actions of impatience mechanisms are shown and compared with evolution without the impatience and with a fitness sharing. The efficiency of crossing saddles is experimentally examined for different fitness functions. Results presented in the paper confirm good properties of the impatience mechanism in diversity maintaining and saddle crossing.

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“…Therefore, the use of stochastic optimization methods such as EAs is recommended. In this study, we compare the standard anisotropic evolution strategy (ES) [4] with two niching EAs [5]: the clearing algorithm (CL) [6] and the restricted tournament selection (RTS) with self-adaptive recombination [7,8]. Moreover, two different chromosome encoding strategies have been implemented for generating signal profiles ( Figure 2).…”

Section: Signal Synthesis Schemesmentioning

confidence: 99%

Signal synthesis by means of evolutionary algorithms

Jaafar

Sareni

Roboam

2011

Inverse Problems in Science and Engineering

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. In this article, we investigate a procedure for generating signals with genetic algorithms. Signals are obtained from elementary patterns characterized by different degrees of freedom. These patterns are repeated and combined in order to reach specific signal shapes. The whole signal parametrization has to be determined by solving a difficult inverse problem of high dimensionality and strong multimodality. This can be carried out using evolutionary algorithms with the aim of finding all pattern configurations in the signal. The different signal synthesis schemes are evaluated, tested and applied to the generation of particular railway driving profiles.

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Fitness sharing and niching methods revisited

Cited by 481 publications

References 9 publications

Parallel surrogate-assisted global optimization with expensive functions – a survey

Parallel surrogate-assisted global optimization with expensive functions – a survey

The impatience mechanism as a diversity maintaining and saddle crossing strategy

Signal synthesis by means of evolutionary algorithms

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