Evolutionary algorithm
In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as 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 quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators.
- Comment
- enIn computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as 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 quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators.
- Date
- enJanuary 2018
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- Has abstract
- enIn computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as 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 quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators. Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape. Techniques from evolutionary algorithms applied to the modeling of biological evolution are generally limited to explorations of microevolutionary processes and planning models based upon cellular processes. In most real applications of EAs, computational complexity is a prohibiting factor. In fact, this computational complexity is due to fitness function evaluation. Fitness approximation is one of the solutions to overcome this difficulty. However, seemingly simple EA can solve often complex problems; therefore, there may be no direct link between algorithm complexity and problem complexity.
- Hypernym
- Subset
- Is primary topic of
- Evolutionary algorithm
- Label
- enEvolutionary algorithm
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- 3-540-31306-0
- 9780367802486
- 978-1-4471-5013-8
- 978-3-662-07807-5
- 978-3-662-44874-8
- Adaptive dimensional search
- Algorithm
- Ant colony optimization
- Artificial bee colony algorithm
- Artificial development
- Average information
- Avida
- Bees algorithm
- Biological evolution
- Bounded set
- Candidate solution
- Cartesian genetic programming
- Category:Cybernetics
- Category:Evolution
- Category:Evolutionary algorithms
- Category:Optimization algorithms and methods
- Combinatorial optimization
- Computational intelligence
- Convergence
- Crossover (genetic algorithm)
- Cuckoo
- Cuckoo search
- David E. Goldberg
- Differential evolution
- Embryogenesis
- Encoding
- Entropy in thermodynamics and information theory
- Estimation of distribution algorithm
- Evolution
- Evolutionary computation
- Evolutionary programming
- Evolution strategy
- Evolvability
- Firefly algorithm
- Fitness approximation
- Fitness function
- Fitness landscape
- Gaussian adaptation
- Gene expression programming
- Genetic algorithm
- Genetic programming
- Genetic recombination
- Genetic representation
- Genotype–phenotype distinction
- Grammatical evolution
- Graph theory
- Hans-Paul Schwefel
- Harmony search
- Heuristic (computer science)
- Hunting Search
- Individual
- Ingo Rechenberg
- Keane's function
- Learning classifier system
- Lévy flight
- Linear genetic programming
- Local search (optimization)
- Loss function
- Macroevolution
- Mean fitness
- Memetic algorithm
- Metaheuristic
- Microevolution
- Monotonic function
- Multi expression programming
- Mutation
- Mutation (genetic algorithm)
- Natural selection
- Neuroevolution
- No free lunch theorem
- Numerical optimization
- Offspring
- Optimization
- Optimization (mathematics)
- Optimization problem
- Optimum
- Panmixia
- Particle swarm optimization
- Phenotype
- Population
- Premature convergence
- Reinforcement learning
- Reproduce
- Reproduction
- Richard Dawkins
- Rosenbrock function
- Sequence
- S-expression
- Subset
- Supervised learning
- Swarm intelligence
- Test functions for optimization
- Tierra (computer simulation)
- Without loss of generality
- Reason
- enWhy are swarm algorithms associated with evolutionary ones?
- SameAs
- Algorithme évolutionniste
- Algoritmo evolutivo
- Algoritmo evolutivo
- Algoritmo evolutivo
- Algoritmo evolutivo
- Algorytm ewolucyjny
- Evolucijski algoritam
- Evolucijski algoritem
- Evolutionära Algorithmus
- Evolutionärer Algorithmus
- Evolutionary algorithm
- Evolutionary algorithm
- Evoluutioalgoritmi
- Evrimsel algoritma
- m.01b23t
- Q14489129
- T9D8
- Təkamül alqoritmləri
- Еволуциони алгоритам
- Еволюційний алгоритм
- Эволюционные алгоритмы
- الگوریتم فرگشتی
- خوارزمية تطورية
- विकासात्मक कलनविधि
- ขั้นตอนวิธีเชิงวิวัฒนาการ
- 进化算法
- 進化的アルゴリズム
- Subject
- Category:Cybernetics
- Category:Evolution
- Category:Evolutionary algorithms
- Category:Optimization algorithms and methods
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- Evolutionary algorithm?oldid=1121859515&ns=0
- WikiPageLength
- 28001
- Wikipage page ID
- 190837
- Wikipage revision ID
- 1121859515
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