0000000000773295

AUTHOR

Carlos Cotta

0000-0001-8478-7549

showing 4 related works from this author

A Primer on Memetic Algorithms

2012

Memetic Algorithms (MAs) are population-based metaheuristics composed of an evolutionary framework and a set of local search algorithms which are activated within the generation cycle of the external framework, see [376]. The earliest MA implementation has been given in [621] in the context of the Travelling Salesman Problem (TSP) while an early systematic definition has been presented in [615]. The concept of meme is borrowed from philosophy and is intended as the unit of cultural transmission. In other words, complex ideas can be decomposed into memes which propagate andmutate within a population.Culture, in this way, constantly undergoes evolution and tends towards progressive improvemen…

education.field_of_studyTheoretical computer scienceComputer sciencebusiness.industrySurvival of the fittestPopulationContext (language use)Travelling salesman problemMemetic algorithmLocal search (optimization)educationbusinessCultural transmission in animalsMetaheuristic
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Memetic algorithms and memetic computing optimization: A literature review

2012

Abstract Memetic computing is a subject in computer science which considers complex structures such as the combination of simple agents and memes, whose evolutionary interactions lead to intelligent complexes capable of problem-solving. The founding cornerstone of this subject has been the concept of memetic algorithms, that is a class of optimization algorithms whose structure is characterized by an evolutionary framework and a list of local search components. This article presents a broad literature review on this subject focused on optimization problems. Several classes of optimization problems, such as discrete, continuous, constrained, multi-objective and characterized by uncertainties…

Structure (mathematical logic)Class (computer programming)Optimization problemGeneral Computer ScienceComputer sciencebusiness.industryGeneral MathematicsEvolutionary algorithmSubject (documents)Simple (abstract algebra)Memetic algorithmLocal search (optimization)Artificial intelligencebusinessSwarm and Evolutionary Computation
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Applications of Evolutionary Computation

2011

EvoCOMPLEX Contributions.- Coevolutionary Dynamics of Interacting Species.- Evolving Individual Behavior in a Multi-agent Traffic Simulator.- On Modeling and Evolutionary Optimization of Nonlinearly Coupled Pedestrian Interactions.- Revising the Trade-off between the Number of Agents and Agent Intelligence.- Sexual Recombination in Self-Organizing Interaction Networks.- Symbiogenesis as a Mechanism for Building Complex Adaptive Systems: A Review.- EvoGAMES Contributions.- Co-evolution of Optimal Agents for the Alternating Offers Bargaining Game.- Fuzzy Nash-Pareto Equilibrium: Concepts and Evolutionary Detection.- An Evolutionary Approach for Solving the Rubik's Cube Incorporating Exact Met…

020301 aerospace & aeronauticsMeta-optimizationbusiness.industryComputer scienceComputer Science::Neural and Evolutionary ComputationEvolutionary algorithm020206 networking & telecommunicationsGenetic programming02 engineering and technologyEvolutionary computation0203 mechanical engineeringEstimation of distribution algorithmGrammatical evolutionGenetic algorithm0202 electrical engineering electronic engineering information engineeringArtificial intelligenceCMA-ESbusiness
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Memetic Algorithms in Continuous Optimization

2012

Intuitively, a set is considered to be discrete if it is composed of isolated elements, whereas it is considered to be continuous if it is composed of infinite and contiguous elements and does not contain “holes”.

Continuous optimizationSet (abstract data type)Mathematical optimizationComputer sciencebusiness.industryDifferential evolutionMemetic algorithmParticle swarm optimizationLocal search (optimization)businessMetaheuristic
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