Research noteGeneralizing the notion of schema in genetic algorithms
References (2)
Cited by (78)
A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks
2015, Applied Mathematical ModellingCitation Excerpt :In the 1990s, different aspects of the GA were extended; Hartl [39] researched the convergence of the algorithm, and Radcliffe [40] and Bean [41] studied the crossover operator (their studies are used here). Miller and Goldberg [49] studied the selection strategies, and Vose [50] and Koza [51] investigated the entire concept of the GA of Holland by proposing genetic programming. More information about genetic algorithm and other evolutionary algorithms can be found in Reeves [52], Mitchell [53], and Baeck’s et al. [54] studies.
Dynamic model of multi-agent social evolutionary algorithm and its convergence
2013, Journal of China Universities of Posts and TelecommunicationsRedefinition of the KMV model's optimal default point based on genetic algorithms - Evidence from Taiwan
2011, Expert Systems with ApplicationsCitation Excerpt :The so-called schema theorem shows that a genetic algorithm automatically allocates an exponentially increasing number of trials to the best observed schemata. This leads to a favorable trader between exploitation of promising directions of the search space and exploration of less-frequented regions of the space (see also Vose, 1991). However, there is no general result guaranteeing the convergence of a genetic algorithm to the global optimum.
Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm
2009, Expert Systems with ApplicationsAn analysis of the locality of binary representations in genetic and evolutionary algorithms
2021, PeerJ Computer ScienceSoft computing in chemical and physical sciences: A shift in computing paradigm
2017, Soft Computing in Chemical and Physical Sciences: A Shift in Computing Paradigm