374:
between the population and belief space. The best individuals of the population can update the belief space via the update function. Also, the knowledge categories of the belief space can affect the population component via the influence function. The influence function can affect population by
570:
Reynolds, R. G., and Ali, M. Z, “Embedding a Social Fabric
Component into Cultural Algorithms Toolkit for an Enhanced Knowledge-Driven Engineering Optimization”, International Journal of Intelligent Computing and Cybernetics (IJICC), Vol. 1, No 4, pp. 356–378,
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Reynolds, R G., and Ali, M Z., Exploring
Knowledge and Population Swarms via an Agent-Based Cultural Algorithms Simulation Toolkit (CAT), in proceedings of IEEE Congress on Computational Intelligence 2007.
564:
R. G. Reynolds, “An
Introduction to Cultural Algorithms, ” in Proceedings of the 3rd Annual Conference on Evolutionary Programming, World Scientific Publishing, pp 131–139, 1994.
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The belief space of a cultural algorithm is divided into distinct categories. These categories represent different domains of knowledge that the population has of the
599:
259:
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A collection of desirable value ranges for the individuals in the population component e.g. acceptable behavior for the agents in population.
559:
567:
Robert G. Reynolds, Bin Peng. Knowledge
Learning and Social Swarms in Cultural Systems. Journal of Mathematical Sociology. 29:1-18, 2005
855:
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82:
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Robert G. Reynolds, Ziad Kobti, Tim Kohler: Agent-Based
Modeling of Cultural Change in Swarm Using Cultural Algorithms
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M. Omran, A novel cultural algorithm for real-parameter optimization. International
Journal of Computer Mathematics,
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that assesses the performance of each individual in population much like in genetic algorithms.
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The population component of the cultural algorithm is approximately the same as that of the
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component. In this sense, cultural algorithms can be seen as an extension to a conventional
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8:
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by the best individuals of the population. The best individuals can be selected using a
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where there is a knowledge component that is called the belief space in addition to the
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History of the search space - e.g. the temporal patterns of the search process
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Specific examples of important events - e.g. successful/unsuccessful solutions
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Information about the domain of the cultural algorithm problem is applied to.
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577:
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430:(this is done by letting the best individuals to affect the belief space)
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652:
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Let the belief space alter the genome of the offspring by using the
287:. Cultural algorithms were introduced by Reynolds (see references).
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228:
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399:(e.g. set domain specific knowledge and normative value-ranges)
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Select the parents to reproduce a new generation of offspring
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Covariance Matrix
Adaptation Evolution Strategy (CMA-ES)
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altering the genome or the actions of the individuals.
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Information about the topography of the search space
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402:Repeat until termination condition is met
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856:No free lunch in search and optimization
302:The belief space is updated after each
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411:Evaluate each individual by using the
405:Perform actions of the individuals in
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426:Update the belief space by using the
851:Interactive evolutionary computation
643:Interactive evolutionary computation
638:Human-based evolutionary computation
633:Evolutionary multimodal optimization
88:Evolutionary multimodal optimization
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889:Evolutionary Computation (journal)
379:Pseudocode for cultural algorithms
14:
931:
113:Promoter based genetic algorithm
661:Cellular evolutionary algorithm
436:
370:Cultural algorithms require an
314:List of belief space categories
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48:Cellular evolutionary algorithm
920:Nature-inspired metaheuristics
537:
1:
757:Bacterial Colony Optimization
549:10.1080/00207160.2015.1067309
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353:
199:Cartesian genetic programming
118:Spiral optimization algorithm
214:Multi expression programming
7:
752:Particle swarm optimization
696:Gene expression programming
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454:Real-parameter optimization
93:Particle swarm optimization
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716:Learning classifier system
706:Natural evolution strategy
204:Linear genetic programming
151:Clonal selection algorithm
103:Natural evolution strategy
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329:Domain specific knowledge
681:Evolutionary programming
628:Evolutionary data mining
609:Evolutionary computation
475:Evolutionary computation
277:evolutionary computation
273:Cultural algorithms (CA)
68:Evolutionary computation
910:Evolutionary algorithms
811:Artificial intelligence
737:Ant colony optimization
520:Stochastic optimization
515:Sociocultural evolution
465:Artificial intelligence
806:Artificial development
676:Differential evolution
623:Evolutionary algorithm
366:Communication protocol
58:Differential evolution
38:Artificial development
29:Evolutionary algorithm
841:Fitness approximation
826:Evolutionary robotics
767:Metaheuristic methods
335:Situational knowledge
209:Grammatical evolution
171:Genetic fuzzy systems
785:Gaussian adaptation
691:Genetic programming
219:Genetic Improvement
190:Genetic programming
123:Self-modifying code
78:Gaussian adaptation
915:Genetic algorithms
732:Swarm intelligence
725:Related techniques
701:Evolution strategy
671:Cultural algorithm
525:Swarm intelligence
422:influence function
341:Temporal knowledge
73:Evolution strategy
53:Cultural algorithm
897:
896:
871:Program synthesis
846:Genetic operators
836:Fitness landscape
790:Memetic algorithm
775:Firefly algorithm
686:Genetic algorithm
510:Social simulation
495:Memetic algorithm
480:Genetic algorithm
450:Social simulation
360:genetic algorithm
347:Spatial knowledge
285:genetic algorithm
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137:Genetic algorithm
98:Memetic algorithm
83:Grammar induction
63:Effective fitness
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861:Machine learning
831:Fitness function
821:Digital organism
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490:Machine learning
413:fitness function
407:population space
388:(choose initial
386:population space
308:fitness function
275:are a branch of
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234:Parity benchmark
128:Polymorphic code
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799:Related topics
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780:Harmony search
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747:Cuckoo search
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505:Metaheuristic
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181:Fly algorithm
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444:optimization
437:Applications
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397:belief space
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297:search space
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291:Belief space
272:
271:
52:
866:Mating pool
616:Main Topics
395:Initialize
384:Initialize
904:Categories
653:Algorithms
531:References
390:population
354:Population
281:population
146:Chromosome
372:interface
323:knowledge
321:Normative
304:iteration
176:Selection
156:Crossover
881:Journals
500:Memetics
459:See also
446:problems
442:Various
161:Mutation
21:a series
19:Part of
551:, 2015.
229:Eurisko
224:Schema
23:on the
571:2008
545:doi
906::
362:.
299:.
601:e
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261:e
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