Analyzing exploration exploitation trade-off by means of P-I similarity index and dictyostelium based genetic algorithm

Inoue, Ken‐ichiro; Hasegawa, Taku; Mori, Naoki; Matsumoto, Keinosuke

doi:10.1109/cec.2015.7257202

Cited by 2 publications

(1 citation statement)

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“…EP/I009809/1. continues [3], [4]. It is well-known that performances of EAs are significantly influenced by landscapes of problems to be solved, and so, exploitation and exploration are related to not only population diversity but also fitness of individuals.…”

Section: Introductionmentioning

confidence: 99%

Exploitation and Exploration Analysis of Elitist Evolutionary Algorithms: A Case Study

Chen,

2020

Preprint

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Known as two cornerstones of problem solving by search, exploitation and exploration are extensively discussed for implementation and application of evolutionary algorithms (EAs). However, only a few researches focus on evaluation and theoretical estimation of exploitation and exploration. Considering that exploitation and exploration are two issues regarding global search and local search, this paper proposes to evaluate them via the success probability and the one-step improvement rate computed in different domains of integration. Then, case studies are performed by analyzing performances of (1+1) random univariate search and (1+1) evolutionary programming on the sphere function and the cheating problem. By rigorous theoretical analysis, we demonstrate that both exploitation and exploration of the investigated elitist EAs degenerate exponentially with the problem dimension n. Meanwhile, it is also shown that maximization of exploitation and exploration can be achieved by setting an appropriate value for the standard deviation σ of Gaussian mutation, which is positively related to the distance from the present solution to the center of the promising region.

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Section: Introductionmentioning

confidence: 99%

Exploitation and Exploration Analysis of Elitist Evolutionary Algorithms: A Case Study

Chen,

2020

Preprint

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Metaheuristic-based vector quantization approach: a new paradigm for neural network-based video compression

Darwish

Almajtomi

2020

Multimed Tools Appl

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Analyzing exploration exploitation trade-off by means of P-I similarity index and dictyostelium based genetic algorithm

Cited by 2 publications

References 8 publications

Exploitation and Exploration Analysis of Elitist Evolutionary Algorithms: A Case Study

Exploitation and Exploration Analysis of Elitist Evolutionary Algorithms: A Case Study

Metaheuristic-based vector quantization approach: a new paradigm for neural network-based video compression

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