This paper presents a real jumping gene genetic algorithm (RJGGA) as an enhancement of the jumping gene genetic algorithm (JGGA) [T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, A jumping gene algorithm for multiobjective r...
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This paper presents a real jumping gene genetic algorithm (RJGGA) as an enhancement of the jumping gene genetic algorithm (JGGA) [T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, A jumping gene algorithm for multiobjective resource management in wideband CDMA systems, The Computer Journal 48 (6) (2005) 749-768;T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, Multiobjective optimization of radio-to-fiber repeater placement using a jumping gene algorithm, in: Proceedings of the IEEE International Conference on Industrial Technology (ICIT 2005), Hong Kong, 2005, pp. 291-296;K.F. Man, T.M. Chan, K.S. Tang, S. Kwong, Jumping-genes in evolutionary computing, in: Proceedings of the IEEE IECON'2004, Busan, 2004, pp, 1268-1272]. JGGA is a relatively new multiobjective evolutionary algorithm (MOEA) that imitates a jumping gene phenomenon discovered by Nobel Laureate McClintock during her work on the corn plants. The main feature of JGGA is that it only has a simple operation in which a transposition of gene(s) is induced within the same or another chromosome in the genetic algorithm (GA) framework. In its initial formulation, the search space solutions are binary-coded and it inherits the customary problems of conventional binary-coded GA (BCGA). This issue motivated us to remodel the JGGA into RJGGA. The performance of RJGGA has been compared to other MOEAs using some carefully chosen benchmark test functions. It has been observed that RJGGA is able to generate non-dominated solutions with a wider spread along the Pareto-optimal front and better address the issues regarding convergence and diversity in multiobjective optimization. (C) 2006 Elsevier Inc. All rights reserved.
To solve the slow convergence rate and local convergence of Simple Genetic Algorithm, an improved genetic algorithm (IGA) with real-coding, elite reservation, 2/4 competitive choosing and adaptive genetic strategy is ...
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ISBN:
(纸本)9780769536101
To solve the slow convergence rate and local convergence of Simple Genetic Algorithm, an improved genetic algorithm (IGA) with real-coding, elite reservation, 2/4 competitive choosing and adaptive genetic strategy is proposed. The experiment shows that the improved algorithm is more effective in realizing the global optimization and promoting evolution efficiency.
Genetic algorithm is a kind of common method to solve nonlinear programming problems. To improve the computational efficiency of the algorithm, a genetic algorithm based on a new real code (NRCGA) was proposed, which ...
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ISBN:
(纸本)9781457721205
Genetic algorithm is a kind of common method to solve nonlinear programming problems. To improve the computational efficiency of the algorithm, a genetic algorithm based on a new real code (NRCGA) was proposed, which could solve a class of nonlinear programming problems. The new real coded strategy can be used to repair all of the infeasible chromosomes by simply sorting and keeping search within the feasible region. NRCGA is more accurate than the existing methods on equality constraint handling. Many examples show that the new algorithm has high search efficiency and strong robustness.
This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by ...
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This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.
The problem of urban rail train energy saving control with specified running time is a typical multi-constrains, non-linear optimization problem. By applying minimum principle to differential motion model of trains, t...
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ISBN:
(纸本)9780769536002
The problem of urban rail train energy saving control with specified running time is a typical multi-constrains, non-linear optimization problem. By applying minimum principle to differential motion model of trains, the energy saving control strategies are obtained. An approach for optimizing problem based on variable-length real matrix coding multi-population genetic algorithm (MPGA) is presented The train running is simulated by a multi-particle simulator considering complicated line conditions and influence of train length. The GA chromosome consisting of a variable-length two dimensional real matrix represents the train control sequence. A variable length operator based on annealing selection is introduced to enhance global search performance. Fitness sharing keeps population's multiplicity. Multi-population parallel search improves convergence rate and evolution stability. The correctness and advancement of the optimization control method have been validated through the simulation platform of train operation.
The optimization of discrete problems is largely encountered in engineering and information domains. Solving these problems with continuous-variables approach then convert the continuous variables to discrete ones doe...
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The optimization of discrete problems is largely encountered in engineering and information domains. Solving these problems with continuous-variables approach then convert the continuous variables to discrete ones does not guarantee the optimal global solution. Evolutionary Algorithms (EAs) have been applied successfully in combinatorial discrete optimization. Here, the mathematical basics of real-coding Genetic Algorithm are presented in addition to three other Evolutionary Algorithms: Particle Swarm Optimization (PSO), Ant Colony Algorithms (ACOA) and Harmony Search (HS). The EAs are presented in as unifying notations as possible in order to facilitate understanding and comparison. Our combinatorial discrete problem example is the famous benchmark case of New-York Water Supply System WSS network. The mathematical construction in addition to the obtained results of real-coding GA applied to this case study (authors), are compared with those of the three other algorithms available in literature. The real representation of GA, with its two operators: mutation and crossover, functions significantly faster than binary and other coding and illustrates its potential as a substitute to the traditional optimization methods for water systems design and planning. The real (actual) representation is very effective and provides two near-optimal feasible solutions to the New York tunnels problem. We found that the four EAs are capable to afford hydraulically-feasible solutions with reasonable cost but our real-coding GA takes more evaluations to reach the optimal or near-optimal solutions compared to other EAs namely the HS. HS approach discovers efficiently the research space because of the random generation of solutions in every iteration, and the ability of choosing neighbor values of solution elements “changing the diameter of the pipe to the next greater or smaller commercial diameter” beside keeping good current solutions. Our proposed promising point to improve the perfo
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