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Subset of evolutionary computation
methods are known. They are metaheuristics and population-based bio-inspired algorithms and evolutionary computation, which itself are part of the field
Evolutionary_algorithm
Population models of evolutionary algorithms
The population model of an evolutionary algorithm (EA) describes the structural properties of its population to which its members are subject. A population
Population model (evolutionary algorithm)
Population_model_(evolutionary_algorithm)
Trial and error problem solvers with a metaheuristic or stochastic optimization character
Evolutionary computation (EC) from computer science is a family of algorithms for global optimization inspired by biological evolution, and a subfield
Evolutionary_computation
Competitive algorithm for searching a problem space
genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA) in
Genetic_algorithm
first been developed in 1999 in the scope of the application of Evolutionary algorithms to computer stereo vision. Unlike the classical image-based approach
Fly_algorithm
Selection is a genetic operator in an evolutionary algorithm (EA). An EA is a metaheuristic inspired by biological evolution and aims to solve challenging
Selection (evolutionary algorithm)
Selection_(evolutionary_algorithm)
Operator used to vary the programming of chromosomes from one generation to the next
Crossover in evolutionary algorithms and evolutionary computation, also called recombination, is a genetic operator used to combine the genetic information
Crossover (evolutionary algorithm)
Crossover_(evolutionary_algorithm)
Kind of evolutionary algorithm
A cellular evolutionary algorithm (cEA) is a kind of evolutionary algorithm (EA) in which individuals cannot mate arbitrarily, but every one interacts
Cellular evolutionary algorithm
Cellular_evolutionary_algorithm
Genetic operation used to add population diversity
genetic diversity of the chromosomes of a population of an evolutionary algorithm (EA), including genetic algorithms in particular. It is analogous to biological
Mutation (evolutionary algorithm)
Mutation_(evolutionary_algorithm)
Evolutionary algorithm with a defined structure
Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover
Evolutionary_programming
Set of parameters for a genetic or evolutionary algorithm
genotype in evolutionary algorithms (EA) is a set of parameters which define a proposed solution of the problem that the evolutionary algorithm is trying
Chromosome (evolutionary algorithm)
Chromosome_(evolutionary_algorithm)
Algorithm for searching a problem space
operations research, a memetic algorithm (MA) is an extension of an evolutionary algorithm (EA) that aims to accelerate the evolutionary search for the optimum
Memetic_algorithm
Application of game theory to evolving populations in biology
Evolutionary game theory (EGT) is the application of game theory to evolving populations in biology. It defines a framework of contests, strategies, and
Evolutionary_game_theory
discovered every run, with no guarantee however. Evolutionary algorithms (EAs) due to their population based approach, provide a natural advantage over
Evolutionary multimodal optimization
Evolutionary_multimodal_optimization
Surrogate Modeling. Entropy 2020, 22, 285. J. -Y. Li, Z. -H. Zhan, C. Wang, H. Jin and J. Zhang, Boosting Data-Driven Evolutionary Algorithm With Localized
Fitness_approximation
Family of stochastic optimization methods
solutions and ending with the model that generates only the global optima. EDAs belong to the class of evolutionary algorithms. The main difference between
Estimation of distribution algorithm
Estimation_of_distribution_algorithm
Subset of artificial intelligence
ultimate model will be. Leo Breiman distinguished two statistical modelling paradigms: the data model and the algorithmic model, wherein "algorithmic model" means
Machine_learning
Metaheuristic proposed by Xin-She Yang
nature-inspired algorithms" (PDF). Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation. pp
Firefly_algorithm
The B-Cell Algorithm Artificial immune system Biologically inspired computing Computational immunology Computational intelligence Evolutionary computation
Clonal_selection_algorithm
Objective function of evolutionary algorithm
important component of evolutionary algorithms (EA), such as genetic programming, evolution strategies or genetic algorithms. An EA is a metaheuristic
Fitness_function
Solving problems using biological models
which work on a population of possible solutions in the context of evolutionary algorithms or in the context of swarm intelligence algorithms, are subdivided
Bio-inspired_computing
Method for aligning biological sequences
particularly in approaches based on evolutionary algorithms. Architectural similarity can be assessed without requiring model training, enabling more efficient
Needleman–Wunsch_algorithm
of genetic algorithm (GA) applications. Bayesian inference links to particle methods in Bayesian statistics and hidden Markov chain models Artificial
List of genetic algorithm applications
List_of_genetic_algorithm_applications
Genetic algorithm for making artificial neural networks
control tasks, the NEAT algorithm often arrives at effective networks more quickly than other contemporary neuro-evolutionary techniques and reinforcement
Neuroevolution of augmenting topologies
Neuroevolution_of_augmenting_topologies
Modelling evolution using differential equations
evolutionary outcomes, they raise questions about attainability and dynamics around them. A model of evolutionary dynamics should include population dynamics
Evolutionary_dynamics
In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute solution suggestions to the
Human-based_genetic_algorithm
Increase in stock value
artificial intelligence that normalizes the data. Evolutionary programming is often paired with other algorithms e.g. artificial neural networks to improve the
IPO_underpricing_algorithm
Optimization technique
usually population-based metaheuristics. Such metaheuristics include ant colony optimization, evolutionary computation such as genetic algorithm or evolution
Metaheuristic
Collective behaviour of entities that swarm
scientists have turned to evolutionary models that simulate populations of evolving animals. Typically these studies use a genetic algorithm to simulate evolution
Swarm_behaviour
Cultural algorithms (CA) are a branch of evolutionary computation where there is a knowledge component that is called the belief space in addition to the
Cultural_algorithm
Process of finding the optimal set of variables for a machine learning algorithm
evolutionary optimization uses evolutionary algorithms to search the space of hyperparameters for a given algorithm. Evolutionary hyperparameter optimization
Hyperparameter_optimization
Paradigm of rule-based machine learning methods
that combine a discovery component (e.g. typically a genetic algorithm in evolutionary computation) with a learning component (performing either supervised
Learning_classifier_system
Optimization algorithm
distribution algorithm (EDA) An evolutionary algorithm that substitutes traditional reproduction operators by model-guided operators. Such models are learned
Ant colony optimization algorithms
Ant_colony_optimization_algorithms
unwanted effect in evolutionary algorithms (EA), a metaheuristic that mimics the basic principles of biological evolution as a computer algorithm for solving
Premature_convergence
Computer system simulating intelligence
application. Evolutionary computation can be seen as a family of methods and algorithms for global optimization, which are usually based on a population of candidate
Computational_intelligence
Form of artificial intelligence
neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules
Neuroevolution
Mathematical modelling of phenotypic evolution
different terms. Evolutionary invasion analysis makes it possible to identify conditions on model parameters for which the mutant population dies out, replaces
Evolutionary invasion analysis
Evolutionary_invasion_analysis
Point in evolutionary space where selection always leads
selection to generate results through evolutionary algorithms. This is therefore another area in which evolutionary attractors have been identified. It
Evolutionary_attractor
Overview of and topical guide to machine learning
learning Evolutionary multimodal optimization Expectation–maximization algorithm FastICA Forward–backward algorithm GeneRec Genetic Algorithm for Rule
Outline_of_machine_learning
Algorithm in computer science
science and operations research, the artificial bee colony algorithm (ABC) is an optimization algorithm based on the intelligent foraging behaviour of honey
Artificial bee colony algorithm
Artificial_bee_colony_algorithm
Iterative simulation method
A parsimonious SVM model selection criterion for classification of real-world data sets via an adaptive population-based algorithm. Neural Computing and
Particle_swarm_optimization
Evolutionary algorithm
programming (GEP) in computer programming is an evolutionary algorithm that creates computer programs or models. These computer programs are complex tree structures
Gene_expression_programming
Population-based search algorithm
computer science and operations research, the bees algorithm is a population-based search algorithm which was developed by Pham, Ghanbarzadeh et al. in
Bees_algorithm
Data structure and types for evolutionary computation
have also been successfully used and tested in evolutionary algorithms (EA) in general and genetic algorithms in particular, although the implementation of
Genetic_representation
Overview of and topical guide to change in the heritable characteristics of organisms
time within a population Evolutionary game theory – Application of game theory to evolving populations in biology Fitness landscape – Model used to visualise
Outline_of_evolution
Method of mathematical optimization
Differential evolution (DE) is an evolutionary algorithm to optimize a problem by iteratively trying to improve a candidate solution with regard to a given
Differential_evolution
IEEE Transactions on Evolutionary Computation, 5 (2001) 17-26 A. Guven, Linear genetic programming for time-series modelling of daily flow rate, J.
Linear_genetic_programming
Statistical concept
statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that
Mixture_model
Evolving computer programs with techniques analogous to natural genetic processes
programming (GP) is an evolutionary algorithm, an artificial intelligence technique mimicking natural evolution, which operates on a population of programs. It
Genetic_programming
Evolutionary algorithm
problems. They belong to the class of evolutionary algorithms and evolutionary computation. An evolutionary algorithm is broadly based on the principle of
CMA-ES
A genetic operator is an operator used in evolutionary algorithms (EA) to guide the algorithm towards a solution to a given problem. There are three main
Genetic_operator
An algorithm is a fundamental set of rules or defined procedures that are typically designed and used to be a simpler way to solve a specific problem
List_of_algorithms
Technological phenomenon with social implications
Algorithmic bias describes systematic and repeatable harmful tendency in a computerized sociotechnical system to create "unfair" outcomes, such as "privileging"
Algorithmic_bias
[citation needed] The imperialist competitive algorithm (ICA), like most of the methods in the area of evolutionary computation, does not need the gradient
List of metaphor-based metaheuristics
List_of_metaphor-based_metaheuristics
Method of selection in selective breeding
breeding and in evolutionary algorithms from computer science, which selects a certain share of fittest individuals from a population for reproduction
Truncation_selection
Numerical optimization algorithm
Natural evolution strategies (NES) are a family of numerical optimization algorithms for black box problems. Similar in spirit to evolution strategies, they
Natural_evolution_strategy
evaluation phase of an evolutionary algorithm or simulation, individuals are assumed to have interacted with all other members of the population in pair-wise encounters
Complete_mixing
Algorithm in computer science
Evolution strategy (ES) from computer science is a subclass of evolutionary algorithms, which serves as an optimization technique. It uses the major genetic
Evolution_strategy
Resource problem in machine learning
Generalized linear algorithms: The reward distribution follows a generalized linear model, an extension to linear bandits. KernelUCB algorithm: a kernelized
Multi-armed_bandit
Mathematical concept
front approximation. SPEA2 (Strength Pareto Evolutionary Algorithm 2), a population-based evolutionary algorithm using Pareto dominance counts for convergence
Multi-objective_optimization
Genetic programming technique
operators in evolutionary algorithms. Although GE was originally described in terms of using an Evolutionary Algorithm, specifically, a Genetic Algorithm, other
Grammatical_evolution
Java toolkit
genetic algorithms, genetic programming, evolution strategies, coevolution, particle swarm optimization, and differential evolution. The framework models iterative
Java Evolutionary Computation Toolkit
Java_Evolutionary_Computation_Toolkit
Condition where selection restores genetic composition
A population can be described as being in an evolutionarily stable state when that population's "genetic composition is restored by selection after a
Evolutionarily_stable_state
metaheuristics. Just as it exists a long list of metaheuristics like evolutionary algorithms, particle swarm, ant colony optimization, simulated annealing,
Parallel_metaheuristic
Solving multiple machine learning tasks at the same time
Automated machine learning (AutoML) Evolutionary computation Foundation model General game playing Human-based genetic algorithm Kernel methods for vector output
Multi-task_learning
Process in bioinformatics that identifies equivalent sites within molecular sequences
of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. Aligned sequences of nucleotide
Sequence_alignment
Types of approximate algorithm
computational models influenced by human brain functions. Finally, evolutionary computation is a term to describe groups of algorithm that mimic natural
Soft_computing
Scientific concept
Evolutionary mismatch (also "mismatch theory" or "evolutionary trap") is the evolutionary biology concept that an organism's previously advantageous trait
Evolutionary_mismatch
Software for statistical analysis of molecular evolution
Molecular Evolutionary Genetics Analysis (MEGA) is computer software for conducting statistical analysis of molecular evolution and for constructing phylogenetic
Molecular Evolutionary Genetics Analysis
Molecular_Evolutionary_Genetics_Analysis
In evolutionary biology, the GARD (Graded Autocatalysis Replication Domain) model is a general kinetic model for homeostatic-growth and fission of
Gard_model
Collective behavior of decentralized, self-organized systems
also extended the social potential fields model to use spring laws as force laws. Evolutionary algorithms (EA), particle swarm optimization (PSO), differential
Swarm_intelligence
Statistical method for molecular phylogenetics
characters for a certain group of taxa and it does not require a model of evolutionary change. MP gives the most simple explanation for a given set of
Bayesian inference in phylogeny
Bayesian_inference_in_phylogeny
Class of rule-based machine learning systems
principles and processes of the vertebrate immune system. The algorithms are typically modeled after the immune system's characteristics of learning and memory
Artificial_immune_system
Algorithmic technique in ecology
(2019-01-24). "Evolutionary algorithms for species distribution modelling: A review in the context of machine learning". Ecological Modelling. 392: 179–195
Species distribution modelling
Species_distribution_modelling
Equations modelling predator–prey cycles
Model Predator–Prey Dynamics with Type-Two Functional Response Predator–Prey Ecosystem: A Real-Time Agent-Based Simulation Lotka-Volterra Algorithmic
Lotka–Volterra_equations
Reproductive success given genetic mutation
evolutionary equations of the studied population dynamics are available, one can algorithmically compute the effective fitness of a given population.
Effective_fitness
Branch of psychology
Evolutionary psychology is a theoretical approach in psychology that examines cognition and behavior from a modern evolutionary perspective. It seeks
Evolutionary_psychology
American computer scientist
education. Goldberg is known for his contributions to genetic algorithms (GAs) and evolutionary computation, particularly in the areas of selection schemes
David_E._Goldberg
List of concepts in artificial intelligence
intelligence. evolutionary algorithm (EA) A subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses
Glossary of artificial intelligence
Glossary_of_artificial_intelligence
Software environment
environment for heuristic and evolutionary algorithms, developed by members of the Heuristic and Evolutionary Algorithm Laboratory (HEAL) at the University
HeuristicLab
Process by which platform algorithms increase the reach of certain content
internationally in 2018, adopted a model in which its primary content surface, the For You feed, is driven almost entirely by algorithmic recommendation rather than
Algorithmic_amplification
Use of math in the study of cancer
cellular populations and the tumor microenvironment. These ecological and evolutionary dynamics can be exploited therapeutically, as the population-level
Mathematical_oncology
Extrapolation method to detect common ancestors
populations, or species to their common ancestors. It is an important application of phylogenetics, the reconstruction and study of the evolutionary relationships
Ancestral_reconstruction
Probabilistic problem-solving algorithm
experiments or Monte Carlo simulations, are a broad class of computational algorithms based on repeated random sampling for obtaining numerical results, conceptualized
Monte_Carlo_method
Academic field
analyzed. The SIR model is one of the most well known algorithms on predicting the spread of global pandemics within an infectious population. S = β ( 1 N
Network_science
ISSN 0016-6731. PMC 1212250. PMID 5364968. Renshaw, Eric (1991). Modeling Biological Populations in Space and Time. Cambridge University Press. pp. 6–9.
List of research methods in biology
List_of_research_methods_in_biology
Biogeography-based optimization (BBO) is an evolutionary algorithm (EA) that optimizes a function by stochastically and iteratively improving candidate
Biogeography-based optimization
Biogeography-based_optimization
Application of computational algorithms, methods and programs to phylogenetic analyses
algorithms, heuristics, and approaches involved in phylogenetic analyses. The goal is to find a phylogenetic tree representing optimal evolutionary ancestry
Computational_phylogenetics
Evolutionary algorithm designed for maximizing manufacturing yield
adaptation (GA), also called normal or natural adaptation (NA) is an evolutionary algorithm designed for the maximization of manufacturing yield due to statistical
Gaussian_adaptation
Grouping a set of objects by similarity
clusters are modeled with both cluster members and relevant attributes. Group models: some algorithms do not provide a refined model for their results
Cluster_analysis
Computer model of genotype–phenotype maps
computational models motivated by genotype–phenotype mappings in biological systems. Artificial development is often considered a sub-field of evolutionary computation
Artificial_development
Branching diagram of evolutionary relationships between organisms
optimal evolutionary ancestry between a set of species or taxa. Computational phylogenetics (also phylogeny inference) focuses on the algorithms involved
Phylogenetic_tree
Principle relating genetic variance to fitness
selection is an idea about genetic variance in population genetics developed by the statistician and evolutionary biologist Ronald Fisher. The proper way of
Fisher's fundamental theorem of natural selection
Fisher's_fundamental_theorem_of_natural_selection
Methods that imitate, replicate or use natural processes
parameters. Evolutionary programming originally aimed at creating optimal "intelligent agents" modelled, e.g., as finite state machines. Genetic algorithms applied
Natural_computing
Evolution of societies
(1820–1903). Models incorporating distinct stages and ideas of linear models of progress not only had a great influence on future evolutionary approaches
Sociocultural_evolution
cognitive optimization (SCO) is a population-based metaheuristic optimization algorithm which was developed in 2002. This algorithm is based on the social cognitive
Social_cognitive_optimization
Methods in evolutionary biology
natural populations, experimental studies, and mathematical models. Interspecific comparisons allow researchers to assess the generality of evolutionary phenomena
Phylogenetic comparative methods
Phylogenetic_comparative_methods
Method of solving computing problems
control or the provision of a global model. One interesting swarm intelligent technique is the Ant Colony algorithm: Ants are behaviorally unsophisticated;
Lateral_computing
Standard example in game theory
simulations of populations have been made, where individuals with low scores die off, and those with high scores reproduce (a genetic algorithm for finding
Prisoner's_dilemma
Solution concept in game theory
An evolutionarily stable strategy (ESS) is a strategy (or set of strategies) that is impermeable when adopted by a population in adaptation to a specific
Evolutionarily stable strategy
Evolutionarily_stable_strategy
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