geneticalgorithm (GA) has emerged as a powerful method for solving a wide range of combinatorial optimisation problems in many fields. This paper presents a hybrid heuristic approach named guided genetic algorithm (G...
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geneticalgorithm (GA) has emerged as a powerful method for solving a wide range of combinatorial optimisation problems in many fields. This paper presents a hybrid heuristic approach named guided genetic algorithm (GGA) for solving the Multidimensional Knapsack Problem (MKP). GGA is a two-step memetic algorithm composed of a data pre-analysis and a modified GA. The pre-analysis of the problem data is performed using an efficiency-based method to extract useful information. This prior knowledge is integrated as a guide in a GA at two stages: to generate the initial population and to evaluate the produced offspring by the fitness function. Extensive experimentation was carried out to examine GGA on the MKP. The main GGA parameters were tuned and a comparative study with other methods was conducted on well-known MKP data. The real impact of GGA was checked by a statistical analysis using ANOVA, t-test and Welch's t-test. The obtained results showed that the proposed approach largely improved standard GA and was highly competitive with other optimisation methods.
guided genetic algorithm and dynamic distributed double guided genetic algorithm are based on nature laws and by the Neo-Darwinism theory. These evolutionary approaches were very successful addressing Maximal Constrai...
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guided genetic algorithm and dynamic distributed double guided genetic algorithm are based on nature laws and by the Neo-Darwinism theory. These evolutionary approaches were very successful addressing Maximal Constraint Satisfaction Problems (Max-CSPs). Our work is inspired by a little mistake when dealing with in these two algorithms guidance. In fact these approaches are guided by the min-conflict heuristic and the template concept. The used template is distorted. So, we introduce a new template concept in order to allow a better guidance. We suggest considering the percentages of violated constraints in place of their number. This concept is, then, applied to guide the geneticalgorithms. In this paper, we compare the latter guided genetic algorithm with our new template guided genetic algorithm. The experimentations show that our new template guidance improves the optimization process to find best solutions in better time. (C) 2015 The Authors. Published by Elsevier B.V.
guided genetic algorithm and dynamic distributed double guided genetic algorithm are based on nature laws and by the Neo-Darwinism theory. These evolutionary approaches were very successful addressing Maximal Constrai...
详细信息
guided genetic algorithm and dynamic distributed double guided genetic algorithm are based on nature laws and by the Neo-Darwinism theory. These evolutionary approaches were very successful addressing Maximal Constraint Satisfaction Problems (Max-CSPs). Our work is inspired by a little mistake when dealing with in these two algorithms guidance. In fact these approaches are guided by the min-conflict heuristic and the template concept. The used template is distorted. So, we introduce a new template concept in order to allow a better guidance. We suggest considering the percentages of violated constraints in place of their number. This concept is, then, applied to guide the geneticalgorithms. In this paper, we compare the latter guided genetic algorithm with our new template guided genetic algorithm. The experimentations show that our new template guidance improves the optimization process to find best solutions in better time.
In modern industry, maintaining continuous machine operations is important for improving production efficiency and reducing costs. Therefore, the smart technology of acoustic monitoring to detect anomalous machine con...
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The article focuses on the development of an algorithm-based optimization to determine the optimal position of the cameras for motion capturing. Topics discussed include advantage of developed algorithms such as reduc...
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The article focuses on the development of an algorithm-based optimization to determine the optimal position of the cameras for motion capturing. Topics discussed include advantage of developed algorithms such as reduction in the number of cameras, calculation of acquisition volume and simulation of performance gains with developed algorithm.
In this paper, an improved guided genetic algorithm is proposed for the job-shop scheduling problem. The proposed method is improved by a geneticalgorithm using multipliers which can be adjusted during the search pro...
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In this paper, an improved guided genetic algorithm is proposed for the job-shop scheduling problem. The proposed method is improved by a geneticalgorithm using multipliers which can be adjusted during the search process. Simulation results based on some benchmark problems demonstrate that the proposed method can find better solutions than the geneticalgorithm and the original guided genetic algorithm. (C) 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(8): 15-22, 2010;Published online in Wiley Inter Science (***). DOI 10.1002/ecj.10263
We propose a guided genetic algorithm (CA) for planning in games. In guided GA, an extra reinforcement component is inserted into the evolution procedure of GA. During each evolution procedure, the reinforcement compo...
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ISBN:
(纸本)9789077381311
We propose a guided genetic algorithm (CA) for planning in games. In guided GA, an extra reinforcement component is inserted into the evolution procedure of GA. During each evolution procedure, the reinforcement component will simulate the execution of a series of actions of an individual before the real trial and adjust the series of actions according to the reinforcement thus try to improve the performance. We then apply it to a Lunar Lander game in which the falling lunar module needs to learn to land on a platform safely. We compare the performance of guided CA and general CA as well as Q-Learning on the game. The result shows that the guided CA could guarantee to reach the goal and achieve much higher performance than general GA and Q-Learning.
Assembly planning calls for the subtle consideration of certain limitation factors such as geometric features and tools so as to work out a specific assembly sequence. From the assembly sequence, all parts will be tur...
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Assembly planning calls for the subtle consideration of certain limitation factors such as geometric features and tools so as to work out a specific assembly sequence. From the assembly sequence, all parts will be turned into a product. It is evident that the degree of complexity of the assembly problem will increase when the number of constraints is larger. Using geneticalgorithms (GAs) to solve the assembly sequence features speed and flexibility can fit the requirements of various domains. In the case of larger constraint assembly problems, however, GAs will generate a large number of infeasible solutions in the evolution procedure, thus reducing the efficiency of the solution-searching process. Traditionally, using GAs is a random and blind-searching procedure in which it is not always the case that the offspring obtained through the evolutionary mechanism will meet the requirements of all limitations. In this study, therefore, guided-GAs are proposed wherein the proper initial population and the alternation of crossover and mutation mechanisms are covered to overcome assembly planning problems that contain large constraints. The optimal assembly sequence is obtained through the combination of guided-GAs and the Connector-based assembly planning context as previously suggested. Finally, practical examples are offered to illustrate the feasibility of guided-GAs. It is found that guided-GAs can effectively solve the assembly planning problem of larger constraints.
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