probability distribution. The parameters are updated via cross-entropy minimization, so as to generate better samples in the next iteration. Reactive search optimization (RSO) advocates the integration of sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems. The word reactive hints at a ready response to events during the search through an internal online feedback loop for the self-tuning of critical parameters. Methodologies of interest for Reactive Search include machine learning and statistics, in particular reinforcement learning, active or query learning, neural networks, and metaheuristics. See also Genetic programming List of genetic algorithm applications Genetic algorithms in signal processing (a.k.a. particle filters) Propagation of schema Universal Darwinism Metaheuristics Learning classifier system Rule-based machine learning References Bibliography Rechenberg, Ingo (1994): Evolutionsstrategie '94, Stuttgart: Fromman-Holzboog. Schwefel, Hans-Paul (1974): Numerische Optimierung von Computer-Modellen (PhD thesis). Reprinted by Birkhäuser (1977). External links Resources Provides a list of resources in the genetic algorithms field An Overview of the History and Flavors of Evolutionary Algorithms Tutorials Genetic Algorithms - Computer programs that "evolve" in ways that resemble natural selection can solve complex problems even their creators do not fully understand An excellent introduction to GA by John Holland and with an application to the Prisoner's Dilemma An online interactive Genetic Algorithm tutorial for a reader to practise or learn how a GA works: Learn step by step or watch global convergence in batch, change the population size, crossover rates/bounds, mutation rates/bounds and selection mechanisms, and add constraints. A Genetic Algorithm Tutorial by Darrell Whitley Computer Science Department Colorado State University An excellent tutorial with much theory "Essentials of Metaheuristics", 2009 (225 p). Free open text by Sean Luke. Global Optimization Algorithms – Theory and Application Genetic Algorithms in Python Tutorial with the intuition behind GAs and Python implementation. Genetic Algorithms evolves to solve the prisoner's dilemma. Written by Robert Axelrod. Evolutionary algorithms Search algorithms Cybernetics Digital organisms sv:Genetisk programmering#Genetisk algoritm