An Observational Analysis of Genetic Operators
15 Feb 2013-International Journal of Computer Applications (Foundation of Computer Science (FCS))-Vol. 63, Iss: 18, pp 24-34
TL;DR: This paper mainly describes the available selection mechanisms as well as the crossover and the mutation operators for genetic algorithm.
Abstract: algorithm is a search heuristic that mimics the natural process of evolution and it generates solution to a very complex NP-Hard problems. Genetic algorithm belongs to the class of evolutionary algorithms (EA) and it generates solution by using nature inspired techniques like selection, crossover and mutation. The performance of the genetic algorithm is mainly depends on the genetic operators. Genetic operators have the capability to maintain the genetic diversity. This paper mainly describes the available selection mechanisms as well as the crossover and the mutation operators. Keywordsalgorithm; Selection mechanism; Crossover; Mutation
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TL;DR: A novel adaptive fuzzy-based parallel genetic algorithm (GA) is proposed that employs the concept of parallel computing in identifying the optimal configuration of the network and a computationally efficient graph encoding method based on Dandelion coding strategy is developed.
Abstract: The problem of finding optimal configuration of automated/smart power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when the time varying nature of loads is taken into account. In this paper, a systematic approach is proposed to determine an optimal long-term reconfiguration schedule. To solve the optimization problem, a novel adaptive fuzzy-based parallel genetic algorithm (GA) is proposed that employs the concept of parallel computing in identifying the optimal configuration of the network. The integration of fuzzy logic into the proposed method enhances the efficiency of the parallel GA by adaptively modifying the migration rates among different processors during the optimization process. A computationally efficient graph encoding method based on Dandelion coding strategy is developed, which automatically generates radial topologies and prevents the construction of infeasible radial networks in the optimization process. In order to consider the dynamic behavior of the load and reduce the load condition scenarios over the year under study, fuzzy C-mean clustering method is utilized. Finally, the performance of the proposed method is demonstrated on a 119-bus distribution network, and is compared with that of conventional single GA and conventional parallel GA.
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01 Jul 2017
TL;DR: The proposed approach uses a new recombination operator named Parent Set crossover, capable of reducing the disruptive action of the recombination process and enhancing its exploitative power, to incorporate the structural properties of the problem into GA mechanisms.
Abstract: Bayesian Network (BN) structure learning is a complex search problem, generally characterized by multimodality and epistasis. Genetic Algorithms (GAs) have been extensively used to pursue the BN structure learning task. This paper presents a new approach which incorporates the structural properties of the problem into GA mechanisms. The proposed approach uses a new recombination operator named Parent Set crossover, capable of reducing the disruptive action of the recombination process and enhancing its exploitative power. The new operator has been compared with a comprehensive set of other crossover operators as part of two genetic strategies: a canonical GA and a GA with an adaptive mutation scheme. All examined crossover operators were applied on both canonical and adaptive GAs and then compared in terms of various performance metrics. The experiments involve performance measures at the end of evolution as well as their convergence behavior across generations. The performance of the proposed method was also compared with the state-of-the-art non-evolutionary BN structure learning algorithms. Results show that the proposed recombination method enhances the algorithmic efficiency over a variety of test cases of different size.
11 citations
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Abstract: Competitive optimization techniques have been developed to address the complexity of integrated water resources management (IWRM) modelling; however, model adaptation due to changing environments is still a challenge. In this paper we employ multi-variable techniques to increase confidence in model-driven decision-making scenarios. Here, water reservoir management was assessed using two evolutionary algorithm (EA) techniques, the epsilon-dominance-driven self-adaptive evolutionary algorithm (e-DSEA) and the Borg multi-objective evolutionary algorithm (MOEA). Many objective scenarios were evaluated to manage flood risk, hydropower generation, water supply, and release sequences over three decades. Computationally, the e-DSEA’s results are generally reliable, robust, effective and efficient when compared directly with the Borg MOEA but both provide decision support model outputs of value.
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TL;DR: The experimental results for different-size problems show that this algorithm provides Pareto fronts very near to the optimal ones, and the effectiveness of this approach was evaluated.
Abstract: This paper addressed an important variant of two-dimensional cutting stock problem. The objective was not only to minimize trim loss, as in traditional cutting stock problems, but rather to minimiz...
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References
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05 Jul 1995
TL;DR: C Culling is near optimal for this problem, highly noise tolerant, and the best known a~~roach in some regimes, and some new large deviation bounds on this submartingale enable us to determine the running time of the algorithm.
Abstract: We analyze the performance of a Genetic Type Algorithm we call Culling and a variety of other algorithms on a problem we refer to as ASP. Culling is near optimal for this problem, highly noise tolerant, and the best known a~~roach . . in some regimes. We show that the problem of learning the Ising perception is reducible to noisy ASP. These results provide an example of a rigorous analysis of GA’s and give insight into when and how C,A’s can beat competing methods. To analyze the genetic algorithm, we view it as a special type of submartingale. We prove some new large deviation bounds on this submartingale w~ich enable us to determine the running time of the algorithm.
4,520 citations
TL;DR: The comparative study shows that Laplace crossover (LX) performs quite well and one of the genetic algorithms defined (Lx–MPTM) outperforms other genetic algorithms.
366 citations
"An Observational Analysis of Geneti..." refers background or methods in this paper
...This operator shows self adaptive behavior [3]....
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...It takes a pair of parents and it produces two offspring’s [3]....
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...e three parents on a low 10 dimensional functions and four parents on a high 20 dimensional functions [3][27]....
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...UNDX was implemented in the steady state genetic algorithm and it is used to solve three difficult large optimization problems [3][29]....
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...Crossover operator blends the genetic information between the chromosomes in order to explore the search space while the mutation operator is used to maintain the genetic diversity in order to avoid the premature convergence problem [3]....
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Proceedings Article•
13 Jul 1999TL;DR: Experimental results using test functions showed SPX works well on functions having multimodality and/or epistasis with a medium number of parents: 3-parent on a low dimensional function or 4 parents on high dimensional functions.
Abstract: In this paper, we proposed simplex crossover (SPX), a multi-parent recombination operator for real-coded genetic algorithms. SPX generates offspring vector values by uniformly sampling values from simplex formed by m (2 ≤ m ≤ number of parameters + 1) parent vectors. The SPX features an independence from of coordinate systems. Experimental results using test functions, which are commonly used in studies of evolutionary algorithms, showed SPX works well on functions having multimodality and/or epistasis with a medium number of parents: 3-parent on a low dimensional function or 4 parents on high dimensional functions.
363 citations
TL;DR: The election of the most adequate evolution model to take out profit from these parent selection mechanisms is tackled and it is confirmed that these three processes may enhance the operation of the parent-centric crossover operators.
245 citations
"An Observational Analysis of Geneti..." refers background in this paper
...The individuals for the next generations are selected randomly from each of the fitness level [6] [10]....
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...The common example is SBX (Deb and Agrawal, 1995) [6]....
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...Their main purpose is to support the individuals with both high fitness function values and high diversity contributions [6]....
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...• PCCOs are self-adaptive crossover operators [6]....
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