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18th EPIA Conference on Artificial Intelligence (EPIA)

作者： Hassan, Ahmed Pillay, Nelishia Univ KwaZulu Natal ZA-3209 Pietermaritzburg South Africa

ISBN: (纸本)9783319653402;9783319653396

The research presented in this paper forms part of the initiative aimed at automating the design of intelligent techniques to make them more accessible to non-experts. This study focuses on automating the hybridization of metaheuristics and parameter tuning of the individual metaheuristics. It is an initial attempt at testing the feasibility to automate this design process. A genetic algorithm is used for this purpose. Each hybrid metaheuristic is a combination of metaheuristics and corresponding parameter values. The genetic algorithm explores the space of these combinations. The genetic algorithm is evaluated by applying it to solve the symmetric travelling salesman problem. The evolved hybrid metaheuristics are found to perform competitively with the manually designed hybrid approaches from previous studies and outperform the metaheuristics applied individually. The study has also revealed the potential reusability of the evolved hybrids. Based on the success of this initial study, different problem domains shall be used to verify the automation approach to the design of hybrid metaheuristics.

关键词： metaheuristics Hybrid metaheuristics meta-genetic algorithm

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Hybrid metaheuristics: An automated approach

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 130卷 132-144页

作者： Hassan, Ahmed Pillay, Nelishia Univ KwaZulu Natal ZA-3201 Pietermaritzburg South Africa Univ Pretoria ZA-0002 Pretoria South Africa

Hybrid metaheuristics have proven to be effective at solving complex real-world problems. However, designing hybrid metaheuristics is extremely time consuming and requires expert knowledge of the different metaheuristics that are hybridized. In previous work, the effectiveness of automating the design of relay hybrid metaheuristics has been established. A genetic algorithm was used to determine the sequence of hybridized metaheuristics and the parameters of the metaheuristics in the hybrid. This study extends this idea by automating the design of each metaheuristic involved in the hybridization in addition to automating the design of the hybridization. A template is specified for each metaheuristic, defining the metaheuristic in terms of components. Manual design of metaheuristics usually involves determining the components of the metaheuristic. In this study, a genetic algorithm is employed to determine the components and parameters for each metaheuristic as well as the sequence of hybridized metaheuristics. The proposed genetic algorithm approach was evaluated by using it to automatically design hybrid metaheuristics for two problem domains, namely, the aircraft landing problem and the two-dimensional bin packing problem. The automatically designed hybrid metaheuristics were found to perform competitively to state-of-the-art hybridized metaheuristics for both problems. Future research will extend these ideas by looking at automating the derivation of metaheuristic algorithms without predefined structures specified by the templates. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Hybrid metaheuristic meta-genetic algorithm Automated design

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Investigating the parameter space of evolutionary algorithms

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BIODATA MINING 2018年第1期11卷 1-14页

作者： Sipper, Moshe Fu, Weixuan Ahuja, Karuna Moore, Jason H. Univ Penn Inst Biomed Informat Philadelphia PA 19104 USA Ben Gurion Univ Negev Dept Comp Sci IL-8410501 Beer Sheva Israel

Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through an extensive series of experiments over multiple evolutionary algorithm implementations and 25 problems we show that parameter space tends to be rife with viable parameters, at least for the problems studied herein. We discuss the implications of this finding in practice for the researcher employing EC.

关键词： Evolutionary algorithms genetic programming meta-genetic algorithm Parameter tuning Hyper-parameter

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genetic algorithmS FOR NONLINEAR MIXED DISCRETE-INTEGER OPTIMIZATION PROBLEMS VIA meta-genetic PARAMETER OPTIMIZATION

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ENGINEERING OPTIMIZATION 1995年第2期24卷 137-159页

作者： WU, SJ CHOW, PT Department of Mechanical Engineering National Sun Yat-Sen University Kaohsiung 804 Taiwan

This paper describes the application of genetic algorithms to nonlinear constrained mixed discrete-integer optimization problems with optimal sets of parameters furnished by a meta-genetic algorithm. genetic algorithms are combinatorial in nature, and therefore are computationally suitable for treating discrete and integer design variables. Careful attention has been paid to modify the genetic algorithms to promote computational efficiency. Some numerical experiments were performed so as to determine the appropriate range of genetic parameter values. Then the meta-genetic algorithm was employed to optimize these parameters to locate the best solution. Three examples are given to demonstrate the effectiveness of the methodology developed in this paper. Four crossover operators have been compared and the results show that a four-point crossover operator performs best.

关键词： genetic algorithmS MIXED DISCRETE-INTEGER OPTIMIZATION meta-genetic algorithm

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