Evolutionary Learning Algorithm

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An Evolutionary Learning Algorithm is an iterative online learning algorithm that consisting of random variation (Reproduction, Mutation, Recombination) followed by selection.



References

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2014

  1. G.S. Hornby and J.B. Pollack. Creating high-level components with a generative representation for body-brain evolution. Artificial Life, 8(3):223–246, 2002.
  2. Jeff Clune, Benjamin Beckmann, Charles Ofria, and Robert Pennock. "Evolving Coordinated Quadruped Gaits with the HyperNEAT Generative Encoding". Proceedings of the IEEE Congress on Evolutionary Computing Special Section on Evolutionary Robotics, 2009. Trondheim, Norway.
  3. J. Clune, C. Ofria, and R. T. Pennock, “How a generative encoding fares as problem-regularity decreases,” in PPSN (G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, and N. Beume, eds.), vol. 5199 of Lecture Notes in Computer Science, pp. 358–367, Springer, 2008.
  4. Ferreira, C., 2001. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems, Vol. 13, issue 2: 87-129.

2009

  • http://en.wikipedia.org/wiki/Evolutionary_algorithm
    • In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses some mechanisms inspired by biological evolution: reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the environment within which the solutions "live" (see also cost function). Evolution of the population then takes place after the repeated application of the above operators. Artificial evolution (AE) describes a process involving individual evolutionary algorithms; EAs are individual components that participate in an AE.
    • 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 - Like genetic programming, only the structure of the program is fixed and its numerical parameters are allowed to evolve;
    • Evolution strategy - Works with vectors of real numbers as representations of solutions, and typically uses self-adaptive mutation rates;

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