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Journal ArticleDOI

Analysis of exploration and exploitation in evolutionary algorithms by ancestry trees

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TLDR
An ancestry tree-based approach to record the evolution history of a population and a number of exploration and exploitation metrics that can be used to better understand inner working of an evolutionary algorithm or in evolutionary algorithm designing phase to develop suitable variation operators with good balance between Exploration and exploitation.
Abstract
This paper introduces an ancestry tree-based approach for exploration and exploitation analysis. The approach introduces a data structure to record the evolution history of a population and a number of exploration and exploitation metrics. Such an approach not only provides insight of how and when the exploration and exploitation influence an evolution process, but also how the genetic structure of an individual is affected. It can be used to better understand inner working of an evolutionary algorithm or in evolutionary algorithm designing phase to develop suitable variation operators with good balance between exploration and exploitation. The approach is applied to the multi-objective 0/1 knapsack problem.

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Citations
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Journal ArticleDOI

Equilibrium optimizer: A novel optimization algorithm

TL;DR: A well-defined “generation rate” term is proved to invigorate EO’s ability in exploration, exploitation, and local minima avoidance, and its performance is statistically similar to SHADE and LSHADE-SPACMA.
Journal ArticleDOI

Exploration and exploitation in evolutionary algorithms: A survey

TL;DR: A fresh treatment is introduced that classifies and discusses existing work within three rational aspects: what and how EA components contribute to exploration and exploitation; when and how Exploration and exploitation are controlled; and how balance between exploration and exploited is achieved.
Journal ArticleDOI

A comprehensive review of firefly algorithms

TL;DR: The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice as mentioned in this paper, and many problems from various areas have been successfully solved using the Firefly algorithm and its variants.

A Exploration and Exploitation in Evolutionary Algorithms: A Survey

TL;DR: In this paper, a good ratio between exploration and exploitation of a search space is defined as the ratio between the probability that a search algorithm is successful and the probability of being successful.
Journal ArticleDOI

Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems

TL;DR: The experimental results suggest that AOA is a high-performance optimization tool with respect to convergence speed and exploration-exploitation balance, as it is effectively applicable for solving complex problems.
References
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Book

Genetic Algorithms + Data Structures = Evolution Programs

TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Journal ArticleDOI

Optimization of Control Parameters for Genetic Algorithms

TL;DR: GA's are shown to be effective for both levels of the systems optimization problem and are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems.
Journal ArticleDOI

Parameter control in evolutionary algorithms

TL;DR: This paper revision the terminology, which is unclear and confusing, thereby providing a classification of such control mechanisms, and surveys various forms of control which have been studied by the evolutionary computation community in recent years.
Proceedings Article

An Investigation of Niche and Species Formation in Genetic Function Optimization

TL;DR: This contribution briefly describes problems preventing niche formation in conventional genetic algorithms, a crowding method for niche formation and analysis of results when optimizing two multimodal functions.
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