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MCACEA
{{More citations needed, date=April 2023 MCACEA (Multiple Coordinated Agents Coevolution Evolutionary Algorithm) is a general framework that uses a single evolutionary algorithm (EA) per agent sharing their optimal solutions to coordinate the evolutions of the EAs populations using cooperation objectives. This framework can be used to optimize some characteristics of multiple cooperating agents in mathematical optimization problems. More specifically, due to its nature in which both individual and cooperation objectives are optimize, MCACEA is used in multi-objective optimization problems. Description and implementation MCACEA, uses multiple EAs (one per each agent) that evolve their own populations to find the best solution for its associated problem according to their individual and cooperation constraints and objective indexes. Each EA is an optimization problem that runs in parallel and that exchanges some information with the others during its evaluation step. This information ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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. 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 appl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fitness Landscape
Fitness may refer to: * Physical fitness, a state of health and well-being of the body * Fitness (biology), an individual's ability to propagate its genes * Fitness (cereal), a brand of breakfast cereals and granola bars * ''Fitness'' (magazine), a women's magazine, focusing on health and exercise * Fitness and figure competition, a form of physique training, related to bodybuilding * Fitness approximation, a method of function optimization evolutionary computation or artificial evolution methodologies * Fitness function, a particular type of objective function in mathematics and computer science * "Fitness", a 2018 song by Lizzo Melissa Viviane Jefferson (born April 27, 1988), known professionally as Lizzo, is an American singer, rapper, and flutist. Born in Detroit, Michigan, she moved to Houston, Texas with her family when she was 10 years old. After college she ... See also * FitNesse, a web server, a wiki, and a software testing tool * Survival of the fittest ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Paradiseo
ParadisEO is a white-box object-oriented framework dedicated to the flexible design of metaheuristics. It uses EO, a template-based, ANSI-C++ compliant computation library. ParadisEO is portable across both Windows system and sequential platforms (Unix, Linux, Mac OS X, etc.). ParadisEO is distributed under the CeCill license and can be used under several environments. Overview ParadisEO is a white-box object-oriented framework dedicated to the reusable design of metaheuristics, hybrid metaheuristics, and parallel and distributed metaheuristics. ParadisEO provides a broad range of features including evolutionary algorithms, local searches, Particle swarm optimization, the most common parallel and distributed models and hybridization mechanisms, etc. This high content and utility encourages its use at International level. ParadisEO is based on a clear conceptual separation of the solution methods from the problems they are intended to solve. This separation confers to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Java Evolutionary Computation Toolkit
ECJ is a freeware evolutionary computation research system written in Java. It is a framework that supports a variety of evolutionary computation techniques, such as genetic algorithms, genetic programming, evolution strategies, coevolution, particle swarm optimization, and differential evolution. The framework models iterative evolutionary processes using a series of pipelines arranged to connect one or more subpopulations of individuals with selection, breeding (such as crossover, and mutation operators that produce new individuals. The framework is open source and is distributed under the Academic Free License. ECJ was created bSean Luke a computer science professor at George Mason University, and is maintained by Sean Luke and a variety of contributors. Features (listed froECJ's project page: General Features: * GUI with charting * Platform-independent checkpointing and logging * Hierarchical parameter files * Multithreading * Mersenne Twister Random Number Generators * Ab ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
MOEA Framework
The MOEA Framework is an open-source evolutionary computation library for Java that specializes in multi-objective optimization. It supports a variety of multiobjective evolutionary algorithms (MOEAs), including genetic algorithms, genetic programming, grammatical evolution, differential evolution, and particle swarm optimization. As a result, it has been used to conduct numerous comparative studies to assess the efficiency, reliability, and controllability of state-of-the-art MOEAs. Features The MOEA Framework is an extensible framework for rapidly designing, developing, executing, and statistically testing multiobjective evolutionary algorithms (MOEAs). It features 25 different state-of-the-art MOEAs and over 80 analytical test problems. It supports NSGA-II, its recently introduced successor NSGA-III epsilon-MOEA, GDE3., and MOEA/D. natively. In addition, it integrates with the JMetal, Platform and Programming Language Independent Interface for Search Algorithms (PISA), ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Interactive Evolutionary Computation
Interactive evolutionary computation (IEC) or aesthetic selection is a general term for methods of evolutionary computation that use human evaluation. Usually human evaluation is necessary when the form of fitness function is not known (for example, visual appeal or attractiveness; as in Dawkins, 1986) or the result of optimization should fit a particular user preference (for example, taste of coffee or color set of the user interface). IEC design issues The number of evaluations that IEC can receive from one human user is limited by user fatigue which was reported by many researchers as a major problem. In addition, human evaluations are slow and expensive as compared to fitness function computation. Hence, one-user IEC methods should be designed to converge using a small number of evaluations, which necessarily implies very small populations. Several methods were proposed by researchers to speed up convergence, like interactive constrain evolutionary search (user intervention) or ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic Operators
A genetic operator is an operator used in genetic algorithms to guide the algorithm towards a solution to a given problem. There are three main types of operators (mutation, crossover and selection), which must work in conjunction with one another in order for the algorithm to be successful. Genetic operators are used to create and maintain genetic diversity (mutation operator), combine existing solutions (also known as chromosomes) into new solutions (crossover) and select between solutions (selection). In his book discussing the use of genetic programming for the optimization of complex problems, computer scientist John Koza has also identified an 'inversion' or 'permutation' operator; however, the effectiveness of this operator has never been conclusively demonstrated and this operator is rarely discussed. Mutation (or mutation-like) operators are said to be '' unary'' operators, as they only operate on one chromosome at a time. In contrast, crossover operators are said to be ''bi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fitness Approximation
Fitness approximationY. JinA comprehensive survey of fitness approximation in evolutionary computation ''Soft Computing'', 9:3–12, 2005 aims to approximate the objective or fitness functions in evolutionary optimization by building up machine learning models based on data collected from numerical simulations or physical experiments. The machine learning models for fitness approximation are also known as meta-models or surrogates, and evolutionary optimization based on approximated fitness evaluations are also known as surrogate-assisted evolutionary approximation.Surrogate-assisted evolutionary computation: Recent advances and future challenges Swarm and Evolutionary Computation, 1(2):61–70, 2011 Fitness approximation in evolutionary optimiz ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fitness Function
{{no footnotes, date=May 2015 A fitness function is a particular type of objective function that is used to summarise, as a single figure of merit, how close a given design solution is to achieving the set aims. Fitness functions are used in genetic programming and genetic algorithms to guide simulations towards optimal design solutions. Genetic programming and algorithms In particular, in the fields of genetic programming and genetic algorithms, each design solution is commonly represented as a string of numbers (referred to as a chromosome). After each round of testing, or simulation, the idea is to delete the ''n'' worst design solutions, and to breed ''n'' new ones from the best design solutions. Each design solution, therefore, needs to be awarded a figure of merit, to indicate how close it came to meeting the overall specification, and this is generated by applying the fitness function to the test, or simulation, results obtained from that solution. The reason that geneti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mathematical Optimization
Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems of sorts arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries. In the more general approach, an optimization problem consists of maxima and minima, maximizing or minimizing a Function of a real variable, real function by systematically choosing Argument of a function, input values from within an allowed set and computing the Value (mathematics), value of the function. The generalization of optimization theory and techniques to other formulations constitutes a large area of applied mathematics. More generally, op ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Evolutionary Robotics
Evolutionary robotics is an embodied approach to Artificial Intelligence (AI) in which robots are automatically designed using Darwinian principles of natural selection. The design of a robot, or a subsystem of a robot such as a neural controller, is optimized against a behavioral goal (e.g. run as fast as possible). Usually, designs are evaluated in simulations as fabricating thousands or millions of designs and testing them in the real world is prohibitively expensive in terms of time, money, and safety. An evolutionary robotics experiment starts with a population of randomly generated robot designs. The worst performing designs are discarded and replaced with mutations and/or combinations of the better designs. This evolutionary algorithm continues until a prespecified amount of time elapses or some target performance metric is surpassed. Evolutionary robotics methods are particularly useful for engineering machines that must operate in environments in which humans have limit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Evolutionary Computation
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. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection) and mutation. 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 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
