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A genetic algorithm tutorial
This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorialExpand
The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best
This paper reports work done over the past three years using rank-based allocation of reproductive trials to suggest that allocating reproductive trials according to rank is superior to tness proportionate reproduction. Expand
Prediction of Software Reliability Using Connectionist Models
The analysis shows that the connectionist approach is capable of developing models of varying complexity and may adapt well across different data sets and exhibit better predictive accuracy. Expand
Fundamental Principles of Deception in Genetic Search
Several theorems concerning the nature of deception and the central role that deception plays in function optimization using genetic algorithms are presented and different methods of dealing with deception and poor linkage during genetic search are presented. Expand
Evaluating Evolutionary Algorithms
Some basic principles that can be used to develop test suites are discussed and the role of test suites as they have been used to evaluate evolutionary search algorithms are examined. Expand
Lamarckian Evolution, The Baldwin Effect and Function Optimization
It is shown that functions exist where simple genetic algorithms without learning as well as Lamarckian evolution converge to the same local optimum, while genetic search utilizing the Baldwin effect converges to the global optimum. Expand
The No Free Lunch and problem description length
The No Free Lunch theorem is reviewed and cast within a simple framework for black-box search. A duality result which relates functions being optimized to algorithms optimizing them is obtained andExpand
A Comparison of Genetic Sequencing Operators
This work compares six sequencing operators that have been developed for use with genetic algorithms and indicates that the eeective-ness of diierent operators is dependent on the problem domain. Expand