A multi-sexual genetic algorithm for multiobjective optimization

Lis, Joanna; Eiben, A. E.

doi:10.1109/icec.1997.592269

Cited by 61 publications

(39 citation statements)

References 3 publications

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“…There are different ways to maintain population diversity in GA-based MOP solution techniques. Some of the main approaches are Pareto Ranking Based Genetic Algorithm [2], Niched Pareto Algorithm [13], Multi-Sexual Genetic Algorithm [15], Vector Evaluated Genetic Algorithm [20], and Nondominated Sorting Genetic Algorithm [22]. The merits of these methods will not be discussed here.…”

Section: Multigender Genetic Algorithmmentioning

confidence: 99%

A multiobjective coverage‐based model for Civilian search and rescue

Asiedu

Rempel

2011

Naval Research Logistics

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Abstract:The Civil Air Search and Rescue Association (CASARA) is a Canada-wide volunteer aviation association that provides air search support services to the Canadian National Search and Rescue (SAR) program. As with any emergency service provider, the locations of CASARA units greatly impact their overall effectiveness. In this article, the optimal location of CASARA units is formulated as a multiobjective maximal covering location problem. The model addresses the objectives of maximizing the coverage, minimizing the number of units, and maximizing the backup coverage of SAR incidents within Canada. A multigender genetic algorithm is proposed to determine a set of nondominated CASARA location configurations. Results are compared with solutions found using commercial integer programming software. It is shown that the nondominated genetic algorithm solutions are near-optimal. These are determined in much less time than comparable solutions using commercial integer programming software.

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Section: Multigender Genetic Algorithmmentioning

confidence: 99%

A multiobjective coverage‐based model for Civilian search and rescue

Asiedu

Rempel

2011

Naval Research Logistics

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“…Lis and Eiben proposed the multi-sexual genetic algorithm (MSGA) for multi-objective optimization [39]. One sex for each criterion was used.…”

Section: Maintaining Population Diversity In Evolutionary Multi-objecmentioning

confidence: 99%

An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

Dreżewski

Doroz

2017

Symmetry

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Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach-the trend-following algorithm. During the experiments historical data from the Warsaw Stock Exchange is used in order to assess the performance of the compared algorithms. Finally, we draw some conclusions from these experiments, showing the strong and weak points of all the techniques.

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“…However, evolution strategies and solution comparisons are different. The synthesis of numerous comparative studies on MOGA (Lis and Eiben 1997;Zitzler and Thiele 1998;Knowles and Corne 1999;Esquivel et al 1999;Deb et al 2000 andLeiva et al 2000) brought us to develop a Multi-Sexual Genetic Algorithm (MSGA) to resolve the present minimization problems. These algorithms are elitists and characterized by a weak number of parameters.…”

Section: Optimization Modelsmentioning

confidence: 99%

Optimal design and management of chlorination in drinking water networks: a multi-objective approach using Genetic Algorithms and the Pareto optimality concept

Nouiri

2017

Appl Water Sci

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This paper presents the development of multiobjective Genetic Algorithms to optimize chlorination design and management in drinking water networks (DWN). Three objectives have been considered: the improvement of the chlorination uniformity (healthy objective), the minimization of chlorine booster stations number, and the injected chlorine mass (economic objectives). The problem has been dissociated in medium and short terms ones. The proposed methodology was tested on hypothetical and real DWN. Results proved the ability of the developed optimization tool to identify relationships between the healthy and economic objectives as Pareto fronts. The proposed approach was efficient in computing solutions ensuring better chlorination uniformity while requiring the weakest injected chlorine mass when compared to other approaches. For the real DWN studied, chlorination optimization has been crowned by great improvement of free-chlorine-dosing uniformity and by a meaningful chlorine mass reduction, in comparison with the conventional chlorination.

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A multi-sexual genetic algorithm for multiobjective optimization

Cited by 61 publications

References 3 publications

A multiobjective coverage‐based model for Civilian search and rescue

A multiobjective coverage‐based model for Civilian search and rescue

An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

Optimal design and management of chlorination in drinking water networks: a multi-objective approach using Genetic Algorithms and the Pareto optimality concept

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