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Supporting polyploidy in genetic algorithms using dominance vectors

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-662-02830-8openalex-snapshot
Genetic Algorithms + Data Structures = Evolution Programs
inferredcites →https://doi.org/10.1007/978-3-662-07418-3openalex-snapshot
Genetic Algorithms + Data Structures = Evolution Programs
inferredcites →https://doi.org/10.5860/choice.27-0936openalex-snapshot
Genetic algorithms in search, optimization, and machine learning
inferredcites →https://doi.org/10.7302/10207openalex-snapshot
The Behavior Of Adaptive Systems Which Employ Genetic And Correlation Algorithms.
inferredcites →https://doi.org/10.7551/mitpress/1090.001.0001openalex-snapshot
Adaptation in Natural and Artificial Systems
inferredcites →https://doi.org/10.7939/r3fb4ws2wopenalex-snapshot
Genetic algorithms for function optimization

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/11539117_144openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/11893295_92openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/3-540-36077-8_32openalex-snapshot
Preserving Diversity through Diploidy and Meiosis for Improved Genetic Algorithm Performance in Dynamic Environments
inferred← citeshttps://doi.org/10.1007/3-540-45712-7_7openalex-snapshot
Performance Measures for Dynamic Environments
inferred← citeshttps://doi.org/10.1007/978-3-540-72964-8_4openalex-snapshot
Tackling Dynamic Problems with Multiobjective Evolutionary Algorithms
inferred← citeshttps://doi.org/10.1007/978-3-540-85068-7_9openalex-snapshot
Identification and Exploitation of Linkage by Means of Alternative Splicing
inferred← citeshttps://doi.org/10.1007/978-3-642-01262-4_4openalex-snapshot
Dynamic Problems and Nature Inspired Meta-heuristics
inferred← citeshttps://doi.org/10.1007/978-3-642-18965-4_9openalex-snapshot
Designing Evolutionary Algorithms for Dynamic Optimization Problems
inferred← citeshttps://doi.org/10.1007/978-3-642-30665-5_5openalex-snapshot
Dynamic Combinatorial Optimization Problems: A Fitness Landscape Analysis
inferred← citeshttps://doi.org/10.1007/978-3-642-38416-5_7openalex-snapshot
BIPOP: A New Algorithm with Explicit Exploration/Exploitation Control for Dynamic Optimization Problems
inferred← citeshttps://doi.org/10.1007/978-3-7091-0646-4_37openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00500-004-0421-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00500-008-0345-5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00500-010-0616-9openalex-snapshot
Dynamic combinatorial optimisation problems: an analysis of the subset sum problem
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inferred← citeshttps://doi.org/10.1016/j.asoc.2009.06.020openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.eswa.2013.11.040openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.eswa.2018.08.007openalex-snapshot
Quantum firefly swarms for multimodal dynamic optimization problems
inferred← citeshttps://doi.org/10.1016/j.ins.2017.08.058openalex-snapshot
Evolutionary and population-based methods versus constructive search strategies in dynamic combinatorial optimization
inferred← citeshttps://doi.org/10.1016/s0922-3487(03)23001-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1038/35076523openalex-snapshot
Evolutionary computation
inferred← citeshttps://doi.org/10.1080/08839514.2013.835237openalex-snapshot
AN ANALYSIS OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS FOR OPTIMIZATION PROBLEMS WITH TIME CONSTRAINTS
inferred← citeshttps://doi.org/10.1080/0952813x.2016.1186230openalex-snapshot
A bi-population based scheme for an explicit exploration/exploitation trade-off in dynamic environments
inferred← citeshttps://doi.org/10.1108/ijicc-07-2015-0026openalex-snapshot
Diploidy in evolutionary algorithms for dynamic optimization problems
inferred← citeshttps://doi.org/10.1109/cec.1999.785498openalex-snapshot
Searching for optima in non-stationary environments
inferred← citeshttps://doi.org/10.1109/cec.1999.785502openalex-snapshot
Memory enhanced evolutionary algorithms for changing optimization problems
inferred← citeshttps://doi.org/10.1109/cec.2002.1006990openalex-snapshot
The formal stochastic framework for comparison of genetic algorithms
inferred← citeshttps://doi.org/10.1109/cec.2004.1331044openalex-snapshot
A genetic algorithm with gene dependent mutation probability for non-stationary optimization problems
inferred← citeshttps://doi.org/10.1109/cec.2004.1331046openalex-snapshot
On the performance of evolutionary algorithms with life-time adaptation in dynamic fitness landscapes
inferred← citeshttps://doi.org/10.1109/cec.2005.1554987openalex-snapshot
Multiobjective optimization for dynamic environments
inferred← citeshttps://doi.org/10.1109/cec.2006.1688467openalex-snapshot
On the Design of Diploid Genetic Algorithms for Problem Optimization in Dynamic Environments
inferred← citeshttps://doi.org/10.1109/cec.2006.1688589openalex-snapshot
Alternative Splicing in Evolutionary Computation: Adaptation in Dynamic Environments
inferred← citeshttps://doi.org/10.1109/e-science.2006.53openalex-snapshot
Dynamic Problems and Nature Inspired Meta-Heuristics
inferred← citeshttps://doi.org/10.1109/eais.2015.7368783openalex-snapshot
A constructive search algorithm for combinatorial dynamic optimization problems
inferred← citeshttps://doi.org/10.1109/gcis.2010.268openalex-snapshot
Adaptive Adjustment of Weight Parameters for Diploid Genetic Algorithm with a Network Structure
inferred← citeshttps://doi.org/10.1109/iccse.2011.6028589openalex-snapshot
Multi-objective optimization in dynamic environment: A review
inferred← citeshttps://doi.org/10.1109/iecon.2000.972466openalex-snapshot
Genetic algorithms for adaptation to dynamic environments - a survey
inferred← citeshttps://doi.org/10.1109/smcia.2005.1466958openalex-snapshot
Worker ants' rule-based genetic algorithms dealing with changing environments
inferred← citeshttps://doi.org/10.1109/tevc.2005.846356openalex-snapshot
Evolutionary Optimization in Uncertain Environments—A Survey
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A Multimodel Prediction Method for Dynamic Multiobjective Evolutionary Optimization
inferred← citeshttps://doi.org/10.1109/tsmca.2002.804818openalex-snapshot
Coping with nonstationary environments: a genetic algorithm using neutral variation
inferred← citeshttps://doi.org/10.1109/tsmcb.2009.2015281openalex-snapshot
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Multiobjective Resource-Constrained Project Scheduling with a Time-Varying Number of Tasks
inferred← citeshttps://doi.org/10.11606/t.59.2013.tde-03062013-083436openalex-snapshot
Computação Evolutiva em Ambientes Dinâmicos
inferred← citeshttps://doi.org/10.15368/theses.2010.17openalex-snapshot
EXPLORING THE RELATIONSHIP OF THE CLOSENESS OF A GENETIC ALGORITHM’S CHROMOSOME ENCODING TO ITS PROBLEM SPACE
inferred← citeshttps://doi.org/10.22215/etd/2000-04575openalex-snapshot
Improving the behavior of the genetic algorithm in a dynamic environment.
inferred← citeshttps://doi.org/10.25916/sut.26284285openalex-snapshot
Applying extremal optimisation to dynamic optimisation problems
inferred← citeshttps://doi.org/10.3390/su11195542openalex-snapshot
Renovation Construction Process Scheduling for Long-Term Performance of Buildings: An Application Case of University Campus
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