Multiobjective Genetic Algorithms

@incollection{citeulike:2996783, abstract = {Many real-world problems from operations research (OR) / management science (MS) are very complex in nature and quite hard to solve by conventional optimization techniques. Since the 1960s there has been being an increasing interest in imitating living beings to solve such kinds of hard optimization problems. Simulating natural evolutionary processes of human beings results in stochastic optimization techniques called evolutionary algorithms (EAs) that can often outperform conventional optimization methods when applied to difficult real-world problems. EAs mostly involve metaheuristic optimization algorithms such as genetic algorithms (GA) [1, 2], evolutionary programming (EP) [3], evolution strategys (ES) [4, 5], genetic programming (GP) [6, 7], learning classifier systems (LCS) [8], swarm intelligence (comprising ant colony optimization (ACO) [9] and particle swarm optimization (PSO) [10, 11]). Among them, genetic algorithms are perhaps the most widely known type of evolutionary algorithms used today.}, citeulike-article-id = {2996783}, doi = {http://dx.doi.org/10.1007/978-1-84800-181-7\_1}, journal = {Network Models and Optimization}, keywords = {evolutionary-computation, genetic-algorithm, metaheuristic, multi-objective}, pages = {1--47}, posted-at = {2008-07-12 20:46:58}, priority = {2}, title = {Multiobjective Genetic Algorithms}, url = {http://dx.doi.org/10.1007/978-1-84800-181-7\_1}, year = {2008} }

TY - CHAP ID - citeulike:2996783 L3 - citeulike-article-id:2996783 N2 - Many real-world problems from operations research (OR) / management science (MS) are very complex in nature and quite hard to solve by conventional optimization techniques. Since the 1960s there has been being an increasing interest in imitating living beings to solve such kinds of hard optimization problems. Simulating natural evolutionary processes of human beings results in stochastic optimization techniques called evolutionary algorithms (EAs) that can often outperform conventional optimization methods when applied to difficult real-world problems. EAs mostly involve metaheuristic optimization algorithms such as genetic algorithms (GA) [1, 2], evolutionary programming (EP) [3], evolution strategys (ES) [4, 5], genetic programming (GP) [6, 7], learning classifier systems (LCS) [8], swarm intelligence (comprising ant colony optimization (ACO) [9] and particle swarm optimization (PSO) [10, 11]). Among them, genetic algorithms are perhaps the most widely known type of evolutionary algorithms used today. JF - Network Models and Optimization EP - 47 TI - Multiobjective Genetic Algorithms SP - 1 KW - evolutionary-computation KW - genetic-algorithm KW - metaheuristic KW - multi-objective PY - 2008/// UR - http://dx.doi.org/10.1007/978-1-84800-181-7_1 ER -