Trial and error problem solvers with a metaheuristic or stochastic optimization character
For the journal, see Evolutionary Computation (journal).
Evolution of a population of random images. Each frame in the animation is a generation showing the best fitness individual with a genome made up of the greyscale level of each patch. Evolution follows 1. evaluate fitness, 2. rank individuals and 3. include genes from next highest fitness individual. Fitness is the error difference with an image of Charles Darwin
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In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.
In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes as well as, depending on the method, mixing parental information. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection), mutation and possibly recombination. As a result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm.
Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings, making them popular in computer science. Many variants and extensions exist, suited to more specific families of problems and data structures. Evolutionary computation is also sometimes used in evolutionary biology as an in silico experimental procedure to study common aspects of general evolutionary processes.
and 28 Related for: Evolutionary computation information
In computer science, evolutionarycomputation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial...
In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionarycomputation, a generic population-based metaheuristic optimization...
methods". Handbook of EvolutionaryComputation. Institute of Physics Publishing. S2CID 3547258. Shir, Ofer M. (2012). "Niching in Evolutionary Algorithms". In...
applied to computational biology. While evolutionarycomputation is not inherently a part of computational biology, computationalevolutionary biology is...
for future PSO improvements". 2013 IEEE Congress on EvolutionaryComputation. EvolutionaryComputation (CEC), 2013 IEEE Congress on. pp. 2337–2344. doi:10...
the IEEE Computational Intelligence Society two years later by including new areas of interest such as fuzzy systems and evolutionarycomputation, which...
Next, neural networks which are computational models influenced by human brain functions. Finally, evolutionarycomputation is a term to describe groups...
turn of the 21st century. Bio-inspired robotics Evolutionarycomputation Bongard, Josh (2013). "Evolutionary Robotics". Communications of the ACM. 56 (8):...
Human-competitive results). Since 2004, the annual Genetic and EvolutionaryComputation Conference (GECCO) holds Human Competitive Awards (called Humies)...
Topologies". EvolutionaryComputation 10 (2): 99-127 Matthew E. Taylor, Shimon Whiteson, and Peter Stone (2006). "Comparing Evolutionary and Temporal...
The Genetic and EvolutionaryComputation Conference (GECCO) is the premier conference in the area of genetic and evolutionarycomputation. GECCO has been...
Plus-One-Recall-Store (PORS) is a language used in evolutionarycomputation and genetic programming. The PORS language consists of two terminal nodes (1...
phylogenetic tree representing optimal evolutionary ancestry between a set of species or taxa. Computational phylogenetics (also phylogeny inference)...
based on ideas of evolution. It belongs to the general class of evolutionarycomputation or artificial evolution methodologies. The 'evolution strategy'...
IEEE Transactions on EvolutionaryComputation is a bimonthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society...
(June 2019). "Data-Driven Evolutionary Optimization: An Overview and Case Studies". IEEE Transactions on EvolutionaryComputation. 23 (3): 442–458. doi:10...
computing or nonstandard computation) is computing by any of a wide range of new or unusual methods. The term unconventional computation was coined by Cristian...
ISBN 3-540-40184-9 The Hitch-Hiker's Guide to EvolutionaryComputation: What's Evolutionary Programming (EP)? Evolutionary Programming by Jason Brownlee (PhD) Archived...
recent growing areas of research in evolutionarycomputation. The term MA is now widely used as a synergy of evolutionary or any population-based approach...
learning (AutoML) Evolutionarycomputation NeuroEvolution of Augmenting Topologies (NEAT) HyperNEAT (A Generative version of NEAT) Evolutionary Acquisition...
as opposed to a single best solution. Evolutionary multimodal optimization is a branch of evolutionarycomputation, which is closely related to machine...
on Evolutionarycomputation. AE - Artificial Evolution Conference CEC - IEEE Congress on EvolutionaryComputation GECCO - Genetic and Evolutionary Computation...
position in general linguistics, especially syntax; and in computational linguistics. Evolutionary linguistics is part of a wider framework of Universal Darwinism...
However, in some cases (for example, preference-based interactive evolutionarycomputation) the relevance is more limited, because there is no guarantee that...
In evolutionarycomputation, differential evolution (DE) is a method that optimizes a problem by iteratively trying to improve a candidate solution with...
nature-inspired models of computation are cellular automata, neural computation, and evolutionarycomputation. More recent computational systems abstracted from...
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