Zbigniew Michalewicz

Handbook of Evolutionary Computation

From the Publisher: Many scientists and engineers now use the paradigms of evolutionary computation (genetic agorithms, evolution strategies, evolutionary programming, genetic programming, classifier systems, and combinations or hybrids thereof) to tackle problems that are either intractable or unrealistically time consuming to solve through traditional computational strategies. Recently there have been vigorous initiatives to promote cross-fertilization between the EC paradigms, and also to combine these paradigms with other approaches such as neural networks to create hybrid systems with enhanced capabilities. To address the need for speedy dissemination of new ideas in these fields, and also to assist in cross-disciplinary communications and understanding, Oxford University Press and the Institute of Physics have joined forces to create a major reference publication devoted to EC fundamentals, models, algorithms and applications. This work is intended to become the standard reference resource for the evolutionary computation community. The Handbook of Evolutionary Computation will be available in loose-leaf print form, as well as in an electronic version that combines both CD-ROM and on-line (World Wide Web) acess to its contents. Regularly published supplements will be available on a subscription basis.

Parameter control in evolutionary algorithms

The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and promising areas of research in evolutionary computation: it has a potential of adjusting the algorithm to the problem while solving the problem. In the paper we: 1) revise the terminology, which is unclear and confusing, thereby providing a classification of such control mechanisms, and 2) survey various forms of control which have been studied by the evolutionary computation community in recent years. Our classification covers the major forms of parameter control in evolutionary computation and suggests some directions for further research.

Evolutionary algorithms for constrained parameter optimization problems

Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods.

Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization

During the last five years, several methods have been proposed for handling nonlinear constraints using evolutionary algorithms (EAs) for numerical optimization problems. Recent survey papers classify these methods into four categories: preservation of feasibility, penalty functions, searching for feasibility, and other hybrids. In this paper we investigate a new approach for solving constrained numerical optimization problems which incorporates a homomorphous mapping between n-dimensional cube and a feasible search space. This approach constitutes an example of the fifth decoder-based category of constraint handling techniques. We demonstrate the power of this new approach on several test cases and discuss its further potential.

Evolutionary Algorithms in Engineering Applications

Evolutionary algorithms are general-purpose search procedures based on the mechanisms of natural selection and population genetics. They are appealing because they are simple, easy to interface, and easy to extend. This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering. It will be useful for engineers, designers, developers, and researchers in any scientific discipline interested in the applications of evolutionary algorithms. The volume consists of five parts, each with four or five chapters. The topics are chosen to emphasize application areas in different fields of engineering. Each chapter can be used for self-study or as a reference by practitioners to help them apply evolutionary algorithms to problems in their engineering domains.

Adaptive evolutionary planner/navigator for mobile robots

Based on evolutionary computation (EC) concepts, we developed an adaptive evolutionary planner/navigator (EP/N) as a novel approach to path planning and navigation. The EP/N is characterized by generality, flexibility, and adaptability. It unifies off-line planning and online planning/navigation processes in the same evolutionary algorithm which 1) accommodates different optimization criteria and changes in these criteria, 2) incorporates various types of problem-specific domain knowledge, and 3) enables good tradeoffs among near-optimality of paths, high planning efficiency, and effective handling of unknown obstacles. More importantly, the EP/N can self-tune its performance for different task environments and changes in such environments, mostly through adapting probabilities of its operators and adjusting paths constantly, even during a robots motion toward the goal.

Adaptation in evolutionary computation: a survey

Adaptation of parameters and operators is one of the most important and promising areas of research in evolutionary computation; it tunes the algorithm to the problem while solving the problem. In this paper we develop a classification of adaptation on the basis of the mechanisms used, and the level at which adaptation operates within the evolutionary algorithm. The classification covers all forms of adaptation in evolutionary computation and suggests further research.

GAVaPS-a genetic algorithm with varying population size

The size of the population can be critical in many applications of genetic algorithms. If the population size is too small, the genetic algorithm may converge too quickly; if it is too large, the genetic algorithm may waste computational resources; the waiting time for an improvement might be too long. We propose an adaptive method for maintaining variable population size, which grows and shrinks together according to some characteristic of the search. The first experimental results indicate some merits of the proposed method.<<ETX>>

Evolutionary algorithms for constrained engineering problems

Abstract Evolutionary computation techniques have been receiving increasing attention regarding their potential as optimization techniques for complex problems. Recently these techniques were applied in the area of industrial engineering; the most-known applications include scheduling and sequencing in manufacturing systems, computer-aided design, facility layout and location problems, distribution and transportation problems, and many others. Industrial engineering problems usually are quite hard to solve due to a high complexity of the objective functions and a significant number of problem-specific constraints; often an algorithm to solve such problems requires incorporation of some heuristic methods. In this paper we concentrate on constraint handling heuristics for evolutionary computation techniques. This general discussion is followed by three test case studies: truss structure optimization problem, design of a composite laminated plate, and the unit commitment problem. These are typical highly constrained engineering problems and the methods discussed here are directly transferrable to industrial engineering problems.

Parameter Setting in Evolutionary Algorithms

One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.