Appendix A - Genetic Algorithm Internals and Advanced Topics Details

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Appendix A - Genetic Algorithm Internals and Advanced Topics Details

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A genetic algorithm is a computational method modeled on biological evolutionary processes that can be used to find the optimum solution to a problem that may have many solutions.  For example, you can use genetic algorithms to find the maximum and the minimum of a function, to find an optimal route on a map, or to minimize the cost of your products.  They can be used to find an optimal set of parameters (such as the optimal number of shares of each type of stock in a portfolio), or to select an optimal sequence of events (such as the shortest route to cover a sales territory).  In other words, genetic algorithms can be powerful tools for finding the best of millions of possible solutions for different real-life problems.


These algorithms have been found to be very powerful in solving optimization problems that appear to be difficult or even unsolvable by traditional methods.  Genetic algorithms use a minimum of information about the problem and they only require a quantitative estimation of the quality of a possible solution.  This makes them easy to use and applicable to most optimization problems.


Probabilistic versus Deterministic Methods

Genetic Algorithms and Traditional Optimum Search Methods

Continuous Crossover

Crossover of Enumerated Chromosomes

Mutation of Continous Chromosomes

Mutation of Enumerated Chromosomes

Parameters of the Evolutionary Operators

Selection Strategies

Binary and Enumerated Chromosomes in GALIB