Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Biologically influenced algorithms and parallelism in non-linear optimization
Biologically influenced algorithms and parallelism in non-linear optimization
Adaptive individuals in evolving populations
From complex environments to complex behaviors
Adaptive Behavior - Special issue on environment structure and behavior
Adaptive Retrieval Agents: Internalizing Local Contextand Scaling up to the Web
Machine Learning - Special issue on information retrieval
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
ARCCHNID: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Finite Markov Chain Analysis of Genetic Algorithms with Niching
Proceedings of the 5th International Conference on Genetic Algorithms
A Neural Network Model for Prognostic Prediction
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Simple Analytical Models of Genetic Algorithms for Multimodal Function Optimization
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Serial and Parallel Genetic Algorithms as Function Optimizers
Proceedings of the 5th International Conference on Genetic Algorithms
A Comparison of Parallel and Sequential Niching Methods
Proceedings of the 6th International Conference on Genetic Algorithms
On Decentralizing Selection Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Feature Selection Via Mathematical Programming
INFORMS Journal on Computing
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model
Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
Life-like agents: internalizing local cues for reinforcement learning and evolution
Life-like agents: internalizing local cues for reinforcement learning and evolution
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Feature selection in unsupervised learning via evolutionary search
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint Method-Based Evolutionary Algorithm (CMEA) for Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Complementing search engines with online web mining agents
Decision Support Systems - Special issue: Web data mining
Feature selection in data mining
Data mining
Adaption in distributed systems: an evolutionary approach
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary model selection in unsupervised learning
Intelligent Data Analysis
A Praxical Solution of the Symbol Grounding Problem
Minds and Machines
Tackling the premature convergence problem in Monte-Carlo localization
Robotics and Autonomous Systems
Optimal ensemble construction via meta-evolutionary ensembles
Expert Systems with Applications: An International Journal
Decentralized evolutionary optimization approach to the p-median problem
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Tournament searching method to feature selection problem
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Decentralized evolutionary agents streamlining logistic network design
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Evolutionary computation and its applications in neural and fuzzy systems
Applied Computational Intelligence and Soft Computing
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Local selection is a simple selection scheme in evolutionary computation. Individual fitnesses are accumulated over time and compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. Local selection, coupled with fitness functions stemming from the consumption of finite shared environmental resources, maintains diversity in a way similar to fitness sharing. However, it is more efficient than fitness sharing and lends itself to parallel implementations for distributed tasks. While local selection is not prone to premature convergence, it applies minimal selection pressure to the population. Local selection is, therefore, particularly suited to Pareto optimization or problem classes where diverse solutions must be covered. This paper introduces ELSA, an evolutionary algorithm employing local selection and outlines three experiments in which ELSA is applied to multiobjective problems: a multimodal graph search problem, and two Pareto optimization problems. In all these experiments, ELSA significantly outperforms other well-known evolutionary algorithms. The paper also discusses scalability, parameter dependence, and the potential distributed applications of the algorithm.