Journal ArticleDOI
The gambler's ruin problem, genetic algorithms, and the sizing of populations
TLDR
A model to predict the convergence quality of genetic algorithms based on the size of the population based on an analogy between selection in GAs and one-dimensional random walks is presented.Abstract:
This paper presents a model to predict the convergence quality of genetic algorithms based on the size of the population. The model is based on an analogy between selection in GAs and one-dimensional random walks. Using the solution to a classic random walk problem---the gambler's ruin---the model naturally incorporates previous knowledge about the initial supply of building blocks (BBs) and correct selection of the best BB over its competitors. The result is an equation that relates the size of the population with the desired quality of the solution, as well as the problem size and difficulty. The accuracy of the model is verified with experiments using additively decomposable functions of varying difficulty. The paper demonstrates how to adjust the model to account for noise present in the fitness evaluation and for different tournament sizes.read more
Citations
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Journal ArticleDOI
An efficient constraint handling method for genetic algorithms
TL;DR: GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter to guide the search towards the constrained optimum.
Book
How to Solve It: Modern Heuristics
TL;DR: In this article, the authors present a set of heuristics for solving problems with probability and statistics, including the Traveling Salesman Problem and the Problem of Who Owns the Zebra.
Journal ArticleDOI
Parameter control in evolutionary algorithms
TL;DR: This paper revision the terminology, which is unclear and confusing, thereby providing a classification of such control mechanisms, and surveys various forms of control which have been studied by the evolutionary computation community in recent years.
Book ChapterDOI
Parameter Control in Evolutionary Algorithms
TL;DR: A classification of different approaches based on a number of complementary features is provided, and special attention is paid to setting parameters on-the-fly, which has the potential of adjusting the algorithm to the problem while solving the problem.
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.