Evolutionary Algorithm Techniques
Similar techniques differ in the implementation details and the nature of the particular applied problem.
- Genetic algorithm - This is the most popular type of EA. One seeks the solution of a problem in the form of strings of numbers (traditionally binary, although the best representations are usually those that reflect something about the problem being solved), by applying operators such as recombination and mutation (sometimes one, sometimes both). This type of EA is often used in optimization problems.
- Genetic programming - Here the solutions are in the form of computer programs, and their fitness is determined by their ability to solve a computational problem.
- Evolutionary programming - Similar to genetic programming, but the structure of the program is fixed and its numerical parameters are allowed to evolve.
- Gene expression programming - Like genetic programming, GEP also evolves computer programs but it explores a genotype-phenotype system, where computer programs of different sizes are encoded in linear chromosomes of fixed length.
- Evolution strategy - Works with vectors of real numbers as representations of solutions, and typically uses self-adaptive mutation rates.
- Memetic algorithm - It is the hybrid form of population based methods. Inspired by the both Darwinian principles of natural evolution and Dawkins’ notion of a meme and viewed as a form of population-based algorithm coupled with individual learning procedures capable of performing local refinements. The focus of the research study is thus to balance been exploration and exploitation in the search.
- Differential evolution - Based on vector differences and is therefore primarily suited for numerical optimization problems.
- Neuroevolution - Similar to genetic programming but the genomes represent artificial neural networks by describing structure and connection weights. The genome encoding can be direct or indirect.
- Learning classifier system
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