John Holland:

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Revised edition of the seminal 1975 book that generated the momentum for the study of complex adaptive systems and genetic algorithms.
Holland had the intuition that the best way to solve a problem is to mimick what biological organisms do to solve their problem of survival: to evolve (through natural selection) and to reproduce (through genetic recombination).
Genetic algorithms apply recursively a series of biologically-inspired operators to a population of potential solutions of a given problem. Each application of operators generates new populations of solutions which should better and better approximate the best solution.
What evolves is not the single individual but the population as a whole.
Genetic algorithms are actually a further refinement of search methods within problem spaces. Genetic algorithms improve the search by incorporating the criterion of "competition".
A measure function computes how "fit" an individual is. The selection process starts from a random population of individual. For each individual of the population the fitness function provides a numeric value for how much the solution is far from the ideal solution. The probability of selection for that individual is made proportional to its "fitness". On the basis of such fitness values a subset of the population is selected. This subset is allowed to reproduce itself through biologically-inspired operators of crossover, mutation and inversion.
Each individual (each point in the space of solutions) is represented as a string of symbols. Each genetic operators perform an operation on the sequence or content of the symbols.
Holland's classifier (which learns new rules to optimize its performance) was the first practical application of genetic algorithms. Its emphasis on competition and coopertation, on feedback and reinforcement, rather than on pre-programmed rules, set it apart from knowledge-based models of intelligence.

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