A study on the effect of multi-parent recombination in real coded genetic algorithms

Tsutsui, Shigeyoshi; Ghosh, Ashish

doi:10.1109/icec.1998.700159

Cited by 102 publications

(116 citation statements)

References 9 publications

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“…In our experimental investigation, first we have embedded some crossover operators, namely, CMX [61,62], ADE [53], PCX [12], and TM [17], one by one in MOEA/D, [73] Table 2 and Table 3. The 1 st column in each of these tables shows the minimum (Min), the 2 nd the maximum (Max), the 3 rd the mean, and the 4 th column shows the standard deviation (Std) of IGDmetric values.…”

Section: Wali Khan Mashwani / --00 (2017) 1-26mentioning

confidence: 99%

Hybrid adaptive evolutionary algorithm based on decomposition

Khan

Salhi

Yeniay

et al. 2017

Applied Soft Computing

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The performance of search operators varies across the different stages of the search/optimisation process of Evolutionary Algorithms (EA). In general, a single search operator may not do well in all these stages when dealing with different optimization and search problems. To mitigate this, adaptive search operator schemes have been introduced. The idea is that when a search operator hits a difficult patch (under-performs) in the search space, the EA scheme "reacts" to that by potentially calling upon a different search operator. Hence, several multiple-search operator schemes have been proposed and employed within EA. In this paper, a Hybrid Adaptive Evolutionary Algorithm Based on Decomposition (HAEA/D) that employs four different crossover operators is suggested. Its performance has been evaluated on the well-known IEEE CEC'09 test instances. HAEA/D has generated promising results which compare well against several well-known algorithms including MOEA/D, on a number of metrics such as the Inverted Generational Distance (IGD), the hyper-volume, the Gamma and Delta functions. These results are included and discussed in this paper.

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Section: Wali Khan Mashwani / --00 (2017) 1-26mentioning

confidence: 99%

Hybrid adaptive evolutionary algorithm based on decomposition

Khan

Salhi

Yeniay

et al. 2017

Applied Soft Computing

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“…In a sense, multi-parent genetic algorithms can be viewed as multi-parent generalization of genetic algorithms. In light of MPGAs, several multi-parent crossovers have been proposed and shown their power in a variety of optimization problems [5,7,15]. However, most of these crossovers are validated empirically; only a few theoretical analyses of multi-parent crossovers are conducted.…”

Section: Introductionmentioning

confidence: 99%

On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection

Ting

2005

Advances in Artificial Life

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Abstract. This paper investigates genetic drift in multi-parent genetic algorithms (MPGAs). An exact model based on Markov chains is proposed to formulate the variation of gene frequency. This model identifies the correlation between the adopted number of parents and the mean convergence time. Moreover, it reveals the pairwise equivalence phenomenon in the number of parents and indicates the acceleration of genetic drift in MPGAs. The good fit between theoretical and experimental results further verifies the capability of this model.

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“…MOEA/D paradigm have tackled diverse benchmark functions and it has several enhanced versions [31], [34], [32], [39], [36], [42]. In this paper, our main objective is to further improve the algorithmic performance of ALMAGAM by employing by employing multiple search operators including the differential evolution (DE) [46], particle swarm optimization (PSO) [15], simulated binary crossover (SBX) [24], Pareto archive evolution strategy (PAES) [23] and simplex crossover (SPX) [50] with self-adaptive alternative procedures for dealing with both CEC'09 test instances [57] and ZDT test problems [62].…”

Section: Introductionmentioning

confidence: 99%

“…The main objective in this paper to develop an enhanced version of ALMAGAM by employing multiple search operators such as differential evolution (DE) [46], particle swarm optimization (PSO) [15], simulated binary crossover (SBX) [24], Pareto archive evolution strategy (PAES) [23] and simplex crossover (SPX) [50] with self-adaptive procedures for dealing with both CEC'09 test instances [57] and ZDT test problems [62].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Version of Multi-algorithm Genetically Adaptive for Multiobjective optimization

Khan¹,

Salhi²,

Asif³

et al. 2015

ijacsa

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Abstract-Multi-objective EAs (MOEAs) are well established population-based techniques for solving various search and optimization problems. MOEAs employ different evolutionary operators to evolve populations of solutions for approximating the set of optimal solutions of the problem at hand in a single simulation run. Different evolutionary operators suite different problems. The use of multiple operators with a selfadaptive capability can further improve the performance of existing MOEAs. This paper suggests an enhanced version of a genetically adaptive multi-algorithm for multi-objective (AMAL-GAM) optimisation which includes differential evolution (DE), particle swarm optimization (PSO), simulated binary crossover (SBX), Pareto archive evolution strategy (PAES) and simplex crossover (SPX) for population evolution during the course of optimization. We examine the performance of this enhanced version of AMALGAM experimentally over two different test suites, the ZDT test problems and the test instances designed recently for the special session on MOEA's competition at the Congress of Evolutionary Computing of 2009 (CEC'09). The suggested algorithm has found better approximate solutions on most test problems in terms of inverted generational distance (IGD) as the metric indicator.

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A study on the effect of multi-parent recombination in real coded genetic algorithms

Cited by 102 publications

References 9 publications

Hybrid adaptive evolutionary algorithm based on decomposition

Hybrid adaptive evolutionary algorithm based on decomposition

On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection

Enhanced Version of Multi-algorithm Genetically Adaptive for Multiobjective optimization

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