"Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems. The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers."--Provided by publisher.

Cover; Title Page; Copyright Page; Dedication; Contents; Preface; Editors; Contributors; 1 Fitness Evaluation in Genetic Algorithms with Ancestorsâ#x80;#x99; Influence; 1.1 Introduction; 1.2 Genetic Algorithms: Basic Principles and Features; 1.3 A New Fitness Evaluation Criterion; 1.3.1 Selection of Weighting Coefficients (α, β, γ); 1.3.2 The Schema Theorem and the Influence of Parents on the Offspring; 1.4 Implementation; 1.4.1 Selection of Genetic Parameters; 1.4.2 Various Schemes; 1.5 Analysis of Results; 1.6 Conclusions; 2 The Walsh Transform and the Theory of the Simple Genetic Algorithm.

2.1 Introduction2.2 Random Heuristic Search; 2.2.1 Notation; 2.2.2 Selection; 2.2.3 Mutation; 2.2.4 Crossover; 2.2.5 The Heuristic Function of the Simple Genetic Algorithm; 2.3 The Walsh Transform; 2.4 The Walsh Basis; 2.5 Invariance; 2.6 The Inverse GA; 2.7 Recombination Limits; 2.8 Conclusion; 3 Adaptation in Genetic Algorithms; 3.1 Introduction; 3.2 Exploitation vs. Exploration in Genetic Algorithms; 3.3 Why Adapt Control Parameters?; 3.4 Adaptive Probabilities of Crossover and Mutation; 3.4.1 Motivations; 3.4.2 Design of Adaptive p[sub(c)] and p[sub(m)].

3.4.3 Practical Considerations and Choice of Values for k[sub(1)], k[sub(2)], and k[sub(3)]3.5 Experiments and Results; 3.5.1 Performance Measures; 3.5.2 Functions for Optimization; 3.5.3 Experimental Results; 3.5.4 When Does the AGA Perform Well?; 3.5.5 Sensitivity of AGA to k[sub(1)] and k[sub(2)]; 3.6 Conclusions; 4 An Empirical Evaluation of Genetic Algorithms on Noisy Objective Functions; 4.1 Introduction; 4.2 Background; 4.3 Empirical Benchmarks; 4.3.1 Algorithm Descriptions; 4.4 Performance Comparisons Using Noisy Fitness Values to Approximate Optimality.

4.4.1 Empirical Results and Analysis4.5 Performance Comparisons Using True Fitness Values in Noisy Optimization Environments; 4.5.1 Empirical Results and Analysis; 4.6 Discussion of Empirical Tests; 4.7 An Application: Geophysical Static Corrections; 4.7.1 Problem Description; 4.7.2 Algorithm Descriptions; 4.7.3 Empirical Results and Analysis; 4.8 Conclusions; 5 Generalization of Heuristics Learned in Genetics-Based Learning; 5.1 Introduction; 5.1.1 Generation of Heuristics; 5.1.2 Testing of Heuristics and Evaluating Their Performance.

5.1.3 Generalization of Heuristics Learned to Unlearned Domains5.2 Performance Evaluation and Anomalies; 5.2.1 Example Applications; 5.2.2 Problem Subspace and Subdomain; 5.2.3 Anomalies in Performance Evaluation; 5.3 Generalization of Heuristic Methods Learned; 5.3.1 Probability of Win within a Subdomain; 5.3.2 Probability of Win across Subdomains; 5.3.3 Generalization Procedure; 5.4 Experimental Results; 5.4.1 Heuristics for Sequential Circuit Testing; 5.4.2 Heuristics for VLSI Placement and Routing; 5.4.3 Branch-and-Bound Search; 5.5 Conclusions.

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