L C Jain

E Vonk

R P Johnson

This book describes the application of evolutionary computation in the automatic generation of a neural network architecture. The architecture has a significant influence on the performance of the neural network. It is the usual practice to use trial and error to find a suitable neural network architecture for a given problem. The process of trial and error is not only time-consuming but may not generate an optimal network. The use of evolutionary computation is a step towards automation in neural network architecture generation. An overview of the field of evolutionary computation is presented, together with the biological background from which the field was inspired. The most commonly used approaches to a mathematical foundation of the field of genetic algorithms are given, as well as an overview of the hybridization between evolutionary computation and neural networks. Experiments on the implementation of automatic neural network generation using genetic programming and one using genetic algorithms are described, and the efficacy of genetic algorithms as a learning algorithm for a feedforward neural network is also investigated. Contents:Artificial Neural NetworksEvolutionary ComputationThe Biological BackgroundMathematical Foundations of Genetic AlgorithmsImplementing GasHybridisation of Evolutionary Computation and Neural NetworksUsing Genetic Programming to Generate Neural NetworksUsing a GA to Optimise the Weights of a Neural NetworkUsing a GA with Grammar Encoding to Generate Neural NetworksConclusions and Future Directions Readership: Scientists, engineers, and researchers interested in artificial intelligence and systems & knowledge engineering. keywords:Artificial Neural Networks;Neural Networworks Architecture;Automatic Neural Networworks Generation;Learning;Genetic Algorithms;Evolutionary Algorithms;Hybridization

Automatic Generation of Neural Network Architecture Using Evolutionary Computation

A F Shapiro

This book presents recent advances in the theory and implementation of intelligent and other computational techniques in the insurance industry. The paradigms covered encompass artificial neural networks and fuzzy systems, including clustering versions, optimization and resampling methods, algebraic and Bayesian models, decision trees and regression splines. Thus, the focus is not just on intelligent techniques, although these constitute a major component; the book also deals with other current computational paradigms that are likely to impact on the industry. The application areas include asset allocation, asset and liability management, cash-flow analysis, claim costs, classification, fraud detection, insolvency, investments, loss distributions, marketing, pricing and premiums, rate-making, retention, survival analysis, and underwriting. Contents:Insurance Applications of Neural Networks, Fuzzy Logic, and Genetic AlgorithmsPractical Applications of Neural Networks in Property and Casualty InsuranceAn Integrated Data Mining Approach to Premium Pricing for the Automobile Insurance IndustryPopulation Risk Management: Reducing Costs and Managing Risk in Health InsuranceUsing Neural Networks to Predict in the MarketplaceMerging Soft Computing Technologies in Insurance-Related ApplicationsRobustness in Bayesian Models for Bonus–Malus SystemsUsing Data Mining for Modeling Insurance Risk and Comparison of Data Mining and Linear Modeling ApproachesSystem Intelligence and Active Stock TradingThe Algebra of Cash Flows: Theory and Applicationand other papers Readership: Graduate students, academics, researchers and practitioners involved with actuarial science, insurance, statistics and management science. Keywords:Insurance;Actuarial Science;Neural Networks;Fuzzy Systems;Computational Intelligence;Computational Techniques;Life and Health Insurance;Property and Casualty Insurance

L C Jain

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