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See:
Description
| Interface Summary | |
|---|---|
| Gene | Base interface for a gene data model in a genome. |
| GeneticAlgorithmProblem | Hook class for problems solved by GeneticAlgorithm. |
| Class Summary | |
|---|---|
| ConcurrenceGeneticAlgorithm | This class is a genetic algorithm that weights its members in comparison to the others. |
| ConcurrenceGeneticAlgorithmBeanInfo | |
| ConcurrenceGeneticAlgorithmBeanInfo.TypePropertyEditor | |
| Gene.BitSet | Bit string gene. |
| Gene.BoundedFloat | Bounded floating point gene data. |
| Gene.BoundedInteger | Bounded integer gene data. |
| Gene.Fixed | Fixed point gene data. |
| Gene.Float | Floating point gene data. |
| Gene.Integer | Integer gene data. |
| Gene.List | Represents a container gene that contains a list of other genes. |
| Gene.Number | Numeric gene data. |
| GeneticAlgorithm | A base class for genetic algorithms. |
| GeneticAlgorithm.Configuration | Algorithmic configuration objects for genetic algorithms. |
| GeneticAlgorithmBeanInfo | |
| Genome | The Genome data in a population represents a state. |
| IncrementalGeneticAlgorithm | An incremental genetic algorithm with overlapping populations and only one reproduction per generation. |
| ParallelEvaluationPopulation | A Population that evaluates its members fitness-weights in parallel. |
| Population | This class represents a population of genomes as a data structure. |
| PopulationBeanInfo | |
| PopulationBeanInfo.PopulationEditor | |
| PopulationImpl | This class implements a population of genomes and provides methods for genetic algorithms. |
| PopulationImplBeanInfo | |
| Selectors | Selection schemes for evolutionary genetic algorithms. |
| SimpleGeneticAlgorithm | A simple genetic algorithm with non-overlapping populations. |
| SteadyStateGeneticAlgorithm | A steady state genetic algorithm with overlapping populations. |
Genetic algorithms simulate nature on a very abstract level to get solutions for sophisticated problems. The simulation corresponds to the current understanding of genetic processes. Especially promising is the usage of genetic algorithms or evolutionary programming for problems where no constructive solution algorithms are known or where they would be far too complex to implement.
With genetic algorithms, problems can be solved by checking whether one of the trial solutions (in the abstract population of data) already is a good solution to the problem. They check whether a member (called an abstract genome) fulfils all criteria required for a good solution of the problem. To create such a solution, trial genomes will be genetically recombined to form new genomes that are new members of the abstract population. Starting with some random genomes, finally one solution will be found - if our basic assumption that the evolution solves our problem in an efficient way, is true.
Genetic algorithms are searching through an arbitrary search space both for exploration and exploitation purpose. In fact, genetic algorithms perform a parallel hill-climbing search through state space (of genomes) maximizing a given fitness function. However, these parallel solutions are occasionally combined to build a new solution of (hopefully) joint force. The search is parallel since several members of a population are considered. Under certain assumptions (Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley. 1989), it can be shown that genetic algorithms allocate searching resources in an optimal way. By the way, genetic algorithms can even be seen as a form of reinforcement learning.
"neural networks are the second best way of doing just about anything" (Denker)
"and genetic algorithms the third"
Prior to using a genetic algorithm to solve a problem you need to decide:
Important classes are:
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Orbital library 1.3.0: 11 Apr 2009 |
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