What is Evolutionary Computation

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Consists on adaptive robust methods with application in search, optimization and learning problems. As the name suggests, these methods were inspired by the principles of genetics and natural evolution of biological organisms. Learn more in: Soft Methods for Automatic Drug Infusion in Medical Care Environment

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Consists on adaptive robust methods with application in search, optimization and learning problems. As the name suggests, these methods were inspired by the principles of genetics and natural evolution of biological organisms. Learn more in: Soft Methods for Automatic Drug Infusion in Medical Care Environment

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Large and diverse class of population-based search algorithms that is inspired by the process of biological evolution through selection, mutation and recombination. They are iterative algorithms that start with an initial population of candidate solutions and then repeatedly apply a series of the genetic operators Learn more in: Evolutionary Grammatical Inference

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Evolutionary algorithms use a population of individuals, where an individual is referred to as a chromosome. A chromosome defines the characteristics of individuals in the population. Each characteristic is referred to as a gene. Learn more in: Intelligence-Based Adaptive Digital Watermarking for Images in Wavelet Transform Domain

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Subfield of artificial intelligence (more particularly computational intelligence) that involves continuous optimization and combinatorial optimization problems. Learn more in: Studying Individualized Transit Indicators Using a New Low-Cost Information System

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Set of Artificial Intelligence techniques used in optimization problems, which are inspired in biologic mechanisms such as natural evolution Learn more in: Evolving Graphs for ANN Development and Simplification

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Subfield of computational intelligence that involves continuous optimization and combinatorial optimization problems and can be considered global optimization methods. Learn more in: Image Segmentation Methods

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A subfield of computational intelligence that involves combinatorial optimization problems. It uses iterative progress, such as growth or development in a population, which is then selected in a guided random search to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution Learn more in: A Hybrid System for Automatic Infant Cry Recognition II

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Evolutionary computation is characterized by an iterative procedure leading to the solution of a guided random search process. Learn more in: Particle Swarm Optimization Algorithm and its Hybrid Variants for Feature Subset Selection

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Collection of techniques, basically aimed at function optimization but applicable to a huge variety of problems, by which the optimum of a function (fitness function) is sought through iterative refinements, according to rules inspired by the laws of natural evolution. Learn more in: Particle Swarm Optimization and Image Analysis

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It is the field of research that deals with the design, and application to engineering problems (e.g., optimization, learning), of bio-inspired algorithms that embody the quintessential characteristics of natural evolutionary systems. Learn more in: Constructivist Apprenticeship through Antagonistic Programming Activities

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refers to a subfield of artificial intelligence or computational intelligence that involves computational algorithms that are inspired by biological processes. Learn more in: Evolutionary Multi-Objective Optimization of Autonomous Mobile Robots in Neural-Based Cognition for Behavioural Robustness

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Set of Artificial Intelligence techniques used in optimization problems, which are inspired in biologic mechanisms such as natural evolution. Learn more in: ANN Development with EC Tools: An Overview

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In computer science evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) that uses iterative progress in a population selected in a guided random search using parallel processing to achieve the desired end. Learn more in: Machine Learning and Financial Investing

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Generic term used to indicate any population-based metaheuristic optimization algorithm that uses mechanisms inspired by biological evolution (Darwin, D., 1859) (Wallace, A. R., 1858), such as reproduction, mutation and recombination. Learn more in: 2D-PAGE Analysis Using Evolutionary Computation

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In computer science it is a subfield of artificial intelligence (more particularly computational intelligence) involving combinatorial optimization problems. Evolutionary computation defines the quite young field of the study of computational systems based on the idea of natural evolution and adaptation. Learn more in: Evolutionary Approaches for ANNs Design

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It refers to the collection of problem-solving approaches that are based on concept of biological evolution. Learn more in: Design of Linear Phase FIR Low Pass Filter Using Mutation-Based Particle Swarm Optimization Technique

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Solution approach guided by biological evolution, which begins with potential solution models, then iteratively applies algorithms to find the fittest models from the set to serve as inputs to the next iteration, ultimately leading to a model that best represents the data. Learn more in: Differential Evolution with Self-Adaptation

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Involves continuous and combinatorial optimization. Its algorithms can be considered global optimization methods of metaheuristic or stochastic nature, proper for solving black box problems. Learn more in: Hybrid Computational Intelligence

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A subfield of artificial intelligence that involve techniques implementing mechanisms inspired by biological evolution such as reproduction, mutation, recombination, natural selection and survival of the fittest. Learn more in: Hybrid Meta-Heuristics Based System for Dynamic Scheduling

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Solution approach guided by biological evolution, which begins with potential solution models, then iteratively applies algorithms to find the fittest models from the set to serve as inputs to the next iteration, ultimately leading to a model that best represents the data. Learn more in: Artificial NeuroGlial Networks

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Solution approach guided by biological evolution, which begins with potential solution models, then iteratively applies algorithms to find the fittest models from the set to serve as inputs to the next iteration, ultimately leading to a model that best represents the data Learn more in: Harmony Search for Multiple Dam Scheduling

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Evolutionary Computation is a stochastic optimization approach which provides a global optimal solution. Learn more in: Evolutionary Computing Approaches to System Identification

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Computation based on iterative progress, such as growth or development in a population. This population is selected in a guided random search using parallel processing to achieve the desired solution. Such processes are often inspired by biological mechanisms of evolution. Learn more in: Multilogistic Regression by Product Units