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Distributed Evolutionary Algorithms in Python (DEAP) is an evolutionary computation framework for rapid prototyping and testing of ideas. [2] [3] [4] It incorporates the data structures and tools required to implement most common evolutionary computation techniques such as genetic algorithm, genetic programming, evolution strategies, particle swarm optimization, differential evolution, traffic ...
The classic example of a mutation operator of a binary coded genetic algorithm (GA) involves a probability that an arbitrary bit in a genetic sequence will be flipped from its original state. A common method of implementing the mutation operator involves generating a random variable for each bit in a sequence.
Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover. [ 1 ] [ 2 ] Evolutionary programming differs from evolution strategy ES( μ + λ {\displaystyle \mu +\lambda } ) only in one detail. [ 1 ]
Crossover in evolutionary algorithms and evolutionary computation, also called recombination, is a genetic operator used to combine the genetic information of two parents to generate new offspring. It is one way to stochastically generate new solutions from an existing population, and is analogous to the crossover that happens during sexual ...
Gene expression programming (GEP) in computer programming is an evolutionary algorithm that creates computer programs or models. These computer programs are complex tree structures that learn and adapt by changing their sizes, shapes, and composition, much like a living organism.
It is a recursive but terminating algorithm, allowing it to avoid infinite recursion. In the "autoconstructive evolution" approach to meta-genetic programming, the methods for the production and variation of offspring are encoded within the evolving programs themselves, and programs are executed to produce new programs to be added to the ...
Evolutionary computation from computer science is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms.
Cartesian genetic programming is a form of genetic programming that uses a graph representation to encode computer programs. It grew from a method of evolving digital circuits developed by Julian F. Miller and Peter Thomson in 1997. [ 1 ]