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This paper critically reviews the reported research on parallel single and multi-objective genetic algorithms. Many early efforts on single and multi-objective genetic algorithms were introduced to reduce the processing time neede...
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This paper critically reviews the reported research on parallel single and multi-objective genetic algorithms. Many early efforts on single and multi-objective genetic algorithms were introduced to reduce the processing time needed to reach an acceptable solution. However, some parallel single and multi-objective genetic algorithms converged to better solutions as compared to comparable sequential single and multiple objective geneticalgorithms.Theauthorsreviewseveralrepresentativemodelsforparallelizingsingleandmulti-objective genetic algorithms. Further, some of the issues that have not yet been studied systematically are identified in the context of parallel single and parallel multi-objective genetic algorithms. Finally, some of the potential applications of parallel multi-objective GAs are discussed.
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A cellular genetic algorithm (CGA) is a decentralized form of GA where individuals in a population are usually arranged in a 2D grid and interactions among individuals are restricted to a set neighborhood. In this paper, we extend...
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A cellular genetic algorithm (CGA) is a decentralized form of GA where individuals in a population are usually arranged in a 2D grid and interactions among individuals are restricted to a set neighborhood. In this paper, we extend the notion of cellularity to memetic algorithms (MA), a configuration termed cellular memetic algorithm (CMA). In addition, we propose adaptive mechanisms that tailor the amount of exploration versus exploitation of local solutions carried out by the CMA. We systematically benchmark this adaptive mechanism and provide evidence that the resulting adaptive CMA outperforms other methods both in the quality of solutions obtained and the number of function evaluations for a range of continuous optimization problems.
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The greedy algorithm is a strong local searching algorithm. The genetic algorithm is generally applied to the global optimization problems. In this paper, we combine the greedy idea and the genetic algorithm to propose the greedy ...
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The greedy algorithm is a strong local searching algorithm. The genetic algorithm is generally applied to the global optimization problems. In this paper, we combine the greedy idea and the genetic algorithm to propose the greedy genetic algorithm which incorporates the global exploring ability of the genetic algorithm and the local convergent ability of the greedy algorithm. Experimental results show that greedy genetic algorithm gives much better results than the classical genetic algorithm.
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Dynamic Time Warping (DTW) is a common technique widely used for nonlinear time normalization of different utterances in many speech recognition systems. Two major problems are usually encountered when the DTW is applied for recog...
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Dynamic Time Warping (DTW) is a common technique widely used for nonlinear time normalization of different utterances in many speech recognition systems. Two major problems are usually encountered when the DTW is applied for recognizing speech utterances: (i) the normalization factors used in a warping path; and (ii) finding the K- best warping paths. Although DTW is modified to compute multiple warping paths by using the Tree-Trellis Search (TTS) algorithm, the use of actual normalization factor still remains a major problem for the DTW.
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This paper presents a parallel genetic simulated annealing (PGSA) algorithm that has been developed and applied to optimize continuous problems. In PGSA, the entire population is divided into sub-populations, and in each sub-popul...
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This paper presents a parallel genetic simulated annealing (PGSA) algorithm that has been developed and applied to optimize continuous problems. In PGSA, the entire population is divided into sub-populations, and in each sub-population the algorithm uses the local search ability of simulated annealing after crossover and mutation. The best individuals of each sub-population are migrated to neighboring ones after a certain number of epochs. An implementation of the algorithm is discussed and the performance is evaluated against a standard set of test functions. PGSA shows some remarkable improvement in comparison with the conventional parallel genetic algorithm and the breeder genetic algorithm (BGA).
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The method of exploration seismics aims at creating an image of the earth's subsurface structures by active acoustic reflection measurements. However, seismic images acquired from land data are often severely degraded due to compl...
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The method of exploration seismics aims at creating an image of the earth's subsurface structures by active acoustic reflection measurements. However, seismic images acquired from land data are often severely degraded due to complex propagation effects near the surface of the earth. Although some methods have been proposed to address the near-surface problem, it remains largely unsolved. We propose a solution that involves an estimation of the true wave propagation effects through the near-surface area in order to compensate for them without explicitly estimating a velocity-depth model. The estimated one-way propagation operators describe wave propagation between the surface level (i.e., the acquisition level) and a laterally consistent datum reflector level. They are parameterized by one-way travel times along a predefined lateral grid. Based on this solution we present a self-adjustable input genetic algorithm (SAIGA) to estimate these travel time functions. SAIGA is an advanced and scalable genetic algorithm that can overcome the hurdle of excessive calculation time due to large 3-D data volumes, as it optimizes the parameters on a representative subset that is randomly selected and periodically updated from the full input dataset. Finally, we apply SAIGA to a 3-D field dataset containing 2.4 million traces yielding good results within a reasonable calculation time.
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Recent breakthroughs in the mathematical estimation of parallel genetic algorithm parameters are applied to the NP-complete problem of scheduling multiple tasks on a cluster of computers connected by a shared bus. Numerous adjustm...
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Recent breakthroughs in the mathematical estimation of parallel genetic algorithm parameters are applied to the NP-complete problem of scheduling multiple tasks on a cluster of computers connected by a shared bus. Numerous adjustments to the original method of parameter estimation were made in order to accurately reflect differences in the problem model. The parallel scheduler used m-ary encoding and included a shared communication bus constraint. Fitness was an indirect computation requiring an evaluation of the meaning and implications (i.e., effect on communication time) of the encoding. The degree of correctness was defined as the "nearness" to the optimal schedule that could be obtained in a limited amount of time. Experiments reveal that the parallel scheduling algorithm developed very accurate schedules when the modified parameter guidelines were used. This article describes the scheduling problem, the parallel genetic scheduler, the adjustments made to the mathematical estimations, the quality of the schedules that were obtained, and the accuracy of the schedules compared to mathematically predicted expected values.
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Genetic algorithm with island and adaptive features has been used for reaching the global optimal solution in the context of structural topology optimization. A two stage adaptive genetic algorithm (TSAGA) involving a self-adaptiv...
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Genetic algorithm with island and adaptive features has been used for reaching the global optimal solution in the context of structural topology optimization. A two stage adaptive genetic algorithm (TSAGA) involving a self-adaptive island genetic algorithm (SAIGA) for the first stage and adaptive techniques in the second stage is proposed for the use in bit-array represented topology optimization. The first stage, consisting a number of island runs each starting with a different set of random population and searching for better designs in different peaks, helps the algorithm in performing an extensive global search. After the completion of island runs the initial population for the second stage is formed from the best members of each island that provides greater variety and potential for faster improvement and is run for a predefined number of generations. In this second stage the genetic parameters and operators are dynamically adapted with the progress of optimization process in such a way as to increase the convergence rate while maintaining the diversity in population. The results obtained on several single and multiple loading case problems have been compared with other GA and non-GA-based approaches, and the efficiency and effectiveness of the proposed methodology in reaching the global optimal solution is demonstrated.
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In this study, a new stopping criterion, called "backward controlled stopping criterion" (BCSC), was proposed to be used in Genetic Algorithms. In the study, the available stopping citeria; adaptive stopping citerion, evolution ti...
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In this study, a new stopping criterion, called "backward controlled stopping criterion" (BCSC), was proposed to be used in Genetic Algorithms. In the study, the available stopping citeria; adaptive stopping citerion, evolution time, fitness threshold, fitness convergence, population convergence, gene convergence, and developed stopping criterion were applied to the following four comparison problems; high strength concrete mix design, pre-stressed precast concrete beam, travelling salesman and reinforced concrete deep beam problems. When completed the analysis, the developed stopping criterion was found to be more accomplished than available criteria, and was able to research a much larger area in the space design supplying higher fitness values.
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Genetic Algorithm is often used to optimize numerical parameters,and Genetic Programming is used to optimize structure.However,optimizing both structure and numerical parameters simultaneously is quite difficult.Many different alg...
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Genetic Algorithm is often used to optimize numerical parameters,and Genetic Programming is used to optimize structure.However,optimizing both structure and numerical parameters simultaneously is quite difficult.Many different algorithms have been developed with the goal of optimizing both structure and numerical parameters simultaneously.However,those algorithms are not satisfactory in the sight of simultaneous optimization.We developed a new algorithm named Genetic Matrix Algorithm (GMA),which can evolve structure and numerical parameters simultaneously.The new algorithm,GMA,can evolve tree-structured programs,which include numerical parameters in each of the nonterminal nodes.In this paper,we apply GMA to evolve image processing algorithms.Experimental results show that we can construct new useful image processing algorithms,which cannot be constructed using only fixed thresholds.
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