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Adaptive Neighborhoods for Cellular Genetic Algorithms

https://doi.org/10.1109/ipdps.2011.168
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What this work says

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

inferredcites →https://doi.org/10.1007/3-540-45356-3_3openalex-snapshot
Cellular Evolutionary Algorithms: Evaluating the Influence of Ratio
inferredcites →https://doi.org/10.1007/978-0-387-77610-1openalex-snapshot
Cellular Genetic Algorithms
inferredcites →https://doi.org/10.1007/b101874openalex-snapshot
Handbook of Metaheuristics
inferredcites →https://doi.org/10.1007/bf00933356openalex-snapshot
Generalized descent for global optimization
inferredcites →https://doi.org/10.1007/bf02430367openalex-snapshot
Test driving three 1995 genetic algorithms: New test functions and geometric matching
inferredcites →https://doi.org/10.1007/s10732-008-9080-4openalex-snapshot
A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization
inferredcites →https://doi.org/10.1016/0378-4754(82)90117-3openalex-snapshot
Numerical optimization of computer models
inferredcites →https://doi.org/10.1023/a:1014803900897openalex-snapshot
Improving flexibility and efficiency by adding parallelism to genetic algorithms
inferredcites →https://doi.org/10.1109/71.663870openalex-snapshot
Parallel genetic simulated annealing: a massively parallel SIMD algorithm
inferredcites →https://doi.org/10.1109/tevc.2002.800880openalex-snapshot
Parallelism and evolutionary algorithms
inferredcites →https://doi.org/10.1109/tevc.2005.843751openalex-snapshot
The Exploration/Exploitation Tradeoff in Dynamic Cellular Genetic Algorithms
inferredcites →https://doi.org/10.1145/197530.197558openalex-snapshot
An evolutionary approach to combinatorial optimization problems
inferredcites →https://doi.org/10.1186/1475-925x-5-47openalex-snapshot
Edited by: Stephan Olariu, Albert Y Zomaya. from Old Dominion University, Norfolk, VA, USA, and University of Sidney, Sidney, Australia, respectively, Chapman & Hall/CRC, Boca Raton, FL, USA; 2006:679pp. 85 contributors, including the editors, from 17 countries
inferredcites →https://doi.org/10.1198/tech.2004.s209openalex-snapshot
Handbook of Parametric and Nonparametric Statistical Procedures
inferredcites →https://doi.org/10.1201/9781420035063openalex-snapshot
Handbook of Bioinspired Algorithms and Applications
inferredcites →https://doi.org/10.1201/9781420036268openalex-snapshot
Handbook of Parametric and Nonparametric Statistical Procedures
inferredcites →https://doi.org/10.1887/0750308958openalex-snapshot
Handbook of Evolutionary Computation
inferredcites →https://doi.org/10.2307/2265725openalex-snapshot
Biostatistical Analysis
inferredcites →https://doi.org/10.7302/10966openalex-snapshot
An Analysis Of The Behavior Of A Class Of Genetic Adaptive Systems.

What points at this work

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

inferred← citeshttps://doi.org/10.1002/9781118833209.ch2openalex-snapshot
Introduction to Evolutionary Algorithms
inferred← citeshttps://doi.org/10.1007/978-3-030-54410-2_37openalex-snapshot
Including Dynamic Adaptative Topology to Particle Swarm Optimization Algorithms
inferred← citeshttps://doi.org/10.1007/978-3-319-69898-4_8openalex-snapshot
Optimization Models with Coalitional Cellular Automata
inferred← citeshttps://doi.org/10.1007/978-3-642-30504-7_19openalex-snapshot
Evolutionary Algorithms Based on Game Theory and Cellular Automata with Coalitions
inferred← citeshttps://doi.org/10.1007/s00500-019-04125-wopenalex-snapshot
The grid-to-neighbourhood relationship in cellular GAs: from design to solving complex problems
inferred← citeshttps://doi.org/10.1007/s11227-012-0773-yopenalex-snapshot
Cellular genetic algorithms without additional parameters
inferred← citeshttps://doi.org/10.1109/icnc.2012.6234715openalex-snapshot
Probabilistic selection in cellular genetic algorithm
inferred← citeshttps://doi.org/10.21608/aunj.2018.221212openalex-snapshot
MIXED VARIABLE OPTIMIZATION MODEL FOR MEDICAL WASTE REVERSE LOGISTICS NETWORKS USING HYBRID CELLULAR GENETIC ALGORITHM
inferred← citeshttps://doi.org/10.21608/fcihib.2022.123469.1068openalex-snapshot
A Hybrid Cellular Genetic Algorithm Based Mixed Variable Optimization Model for Hospital Waste Closed Loop Supply Chain Loop Reverse logistic Networks
inferred← citeshttps://doi.org/10.4028/www.scientific.net/amr.945-949.3130openalex-snapshot
Solving the Problem of General Job Shop Problem by Using Improved Cellular Genetic Algorithm
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