The Importance of Proper Diversity Management in Evolutionary Algorithms for Combinatorial Optimization

Segura, Carlos; Aguirre, Arturo Hernández; Valdez, S. Ivvan; Rionda, Salvador Botello

doi:10.1007/978-3-319-44003-3_6

Cited by 5 publications

(3 citation statements)

References 48 publications

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“…As a result, our proposal is a uniprocess-driven strategy. Among the different strategies, the replacement-based methods have reported quite promising results with several optimization problems [26,23].…”

Section: Control Of Diversity In Easmentioning

confidence: 99%

“…Depending on the replacement phase, additional parameters might be required. In order to set them, some initial experiments were developed by considering some values that have reported promising results [23] in other problems, as well as some modifications of these values. The ones that obtained the best performance are the following.…”

Section: Experimental Validationmentioning

confidence: 99%

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Memetic Algorithm with Hungarian Matching Based Crossover and Diversity Preservation

Ruiz¹,

Segura²

2018

CyS

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The Graph Partitioning Problem (GPP) is a well-known NP-hard combinatorial problem that involves the finding of a partition of vertexes that minimizes the number of cut edges while fulfilling a set of constraints.This paper presents a newly designed optimizer for the GPP: the Memetic Algorithm with Hungarian Matching Based Crossover and Diversity Preservation (MAHMBCDP). MAHMBCDP is a population-based scheme that incorporates an explicit mechanism to control the diversity with the aim of making a proper use of resources when dealing with long-term executions. Among the novelties of our proposal, the design of a crossover operator that is based on the Hungarian Algorithm to calculate a maximum matching is particularly important. Experimental validation with a set of well-known instances of the graph partitioning archive shows the proper performance of our proposal. In fact, new best-known solutions could be attained in ten test cases.

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Section: Control Of Diversity In Easmentioning

confidence: 99%

Section: Experimental Validationmentioning

confidence: 99%

Memetic Algorithm with Hungarian Matching Based Crossover and Diversity Preservation

Ruiz¹,

Segura²

2018

CyS

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“…is considered a cornerstone for proper performance [33]. Since there is a clear relationship between this balance and the amount of diversity maintained in the population, several strategies to alter the amount of population's diversity have been devised [35].…”

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confidence: 99%

GP-DMD: a genetic programming variant with dynamic management of diversity

Nieto-Fuentes

Segura

2022

Genet Program Evolvable Mach

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The proper management of diversity is essential to the success of Evolutionary Algorithms. Specifically, methods that explicitly relate the amount of diversity maintained in the population to the stopping criterion and elapsed period of execution, with the aim of attaining a gradual shift from exploration to exploitation, have been particularly successful.However, in the area of Genetic Programming (GP), the performance of this design principle has not been studied. In this paper, a novel GP variant, Genetic Programming with Dynamic Management of Diversity (GP-DMD), is presented. GP-DMD applies this novel design principle through a replacement strategy that combines penalties based on distance-like functions with a multi-objective Pareto selection based on maximizing accuracy and minimizing tree size. The experimental validation based on the symbolic regression task considers several state-of-the-art approaches and shows clear improvements both in terms of accuracy and simplicity. The effects of GP-DMD on the dynamics of the population are also analyzed, revealing the reasons for the superiority of GP-DMD. As in other fields of Evolutionary Computation, this design principle contributes significantly to the area of GP.

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