Book ChapterDOI
Empirically-Derived Population Size and Mutation Rate Guidelines for a Genetic Algorithm with Uniform Crossover
Edwin A. Williams,William A. Crossley +1 more
- pp 163-172
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TLDR
The results of an empirical study are presented to determine guidelines to assist in choosing appropriate population sizes and mutation rates when using the uniform crossover by examining several parameter combinations on four mathematical functions and one engineering design problem.Abstract:
The Genetic Algorithm (GA) is employed by different users to solve many problems; however, various challenges and issues surround the appropriate form and parameter settings of the GA. One of these issues is the conflict between theory and experiment regarding the crossover operator. Experimental results suggest that the uniform crossover can provide better results for optimization, so many users wish to employ this approach. Unlike for the single-point crossover GA, no established set of guidelines exists to assist in choosing appropriate population sizes and mutation rates when using the uniform crossover. This paper presents the results of an empirical study to determine such guidelines by examining several parameter combinations on four mathematical functions and one engineering design problem. The resulting guidelines appear to be valid over these test problems. They are presented and discussed, with the intent that they may provide assistance to users of GAs with uniform crossover.read more
Citations
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Proceedings ArticleDOI
A comparison of particle swarm optimization and the genetic algorithm
TL;DR: This paper attempts to examine the claim that PSO has the same effectiveness (finding the true global optimal solution) as the GA but with significantly better computational efficiency by implementing statistical analysis and formal hypothesis testing.
Journal ArticleDOI
Multiobjective Multifactorial Optimization in Evolutionary Multitasking
TL;DR: This paper presents a realization of the evolutionary multitasking paradigm within the domain of multiobjective optimization, which leads to the possibility of automated transfer of information across different optimization exercises that may share underlying similarities, thereby facilitating improved convergence characteristics.
Enhancing Aircraft Conceptual Design using Multidisciplinary Optimization
TL;DR: Research into the improvement of the Aircraft ConceptualDesign process by the application of MultidisciplinaryOptimization (MDO) is presented.
Journal ArticleDOI
Aerodynamic and Aeroacoustic Optimization of Rotorcraft Airfoils via a Parallel Genetic Algorithm
TL;DR: A parallel genetic algorithm (GA) methodology was developed to generate a family of two-dimensional airfoil designs that address rotorcraft aerodynamic and aeroacoustic concerns and exhibited favorable performance when compared with typical rotorcraft airfoils under identical design conditions using the same analysis routines.
Journal ArticleDOI
Cognizant Multitasking in Multiobjective Multifactorial Evolution: MO-MFEA-II
TL;DR: This article presents a realization of a cognizant evolutionary multitasking engine within the domain of multiobjective optimization that learns intertask relationships based on overlaps in the probabilistic search distributions derived from data generated during the course of multitasking—and accordingly adapts the extent of genetic transfers online.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
BookDOI
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
TL;DR: Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways.