We propose novel evolutionary algorithms for solving single- and multi-objective political redistricting problems. The objectives include population equality, compactness of districts, deviation from the current distr...
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In this paper, the development of an advanced web tool for the optimization of antenna positioning based on evolutionary algorithms is presented. The system includes a web interface that allows the user to configure t...
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With the rapid development of next-generation sequencing and high-throughput technologies, much biological data have been generated. The analysis of biological networks is becoming a hot topic in bioinformatics in rec...
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Today's volatile market conditions in electronic industries have lead to a new production system,seru(which is the Japanese pronunciation for cell),and has been widely implemented in hundreds of Japanese and other...
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Today's volatile market conditions in electronic industries have lead to a new production system,seru(which is the Japanese pronunciation for cell),and has been widely implemented in hundreds of Japanese and other Asia *** particular,the rotating seru has been widely implemented,where workers are fully cross-trained with the same skill level but may be different on the proficiency of performing *** rotating seru production problem,which determines the rotating sequence of workers as well as the assembling sequence of jobs,is difficult to solve due to conflicting objectives and dynamic release of customer *** solve this problem,we propose a dynamic multiobjective NSGA-II based memetic ***,to preserve desirable population diversity and improve the searching efficiency,we propose different problem-specific evolutionary ***,we test the performance of our proposed memetic algorithm with other state-of-the-art multi-objective evolutionary algorithms and demonstrate the effectiveness of our proposed algorithm.
The application of semantic technologies, particularly ontologies, in the realm of multi -objective evolutionary algorithms is overlook despite their effectiveness in knowledge representation. In this paper, we introd...
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The application of semantic technologies, particularly ontologies, in the realm of multi -objective evolutionary algorithms is overlook despite their effectiveness in knowledge representation. In this paper, we introduce MOODY, an ontology specifically tailored to formalize these kinds of algorithms, encompassing their respective parameters, and multi -objective optimization problems based on a characterization of their search space landscapes. MOODY is designed to be particularly applicable in automatic algorithm configuration, which involves the search of the parameters of an optimization algorithm to optimize its performance. In this context, we observe a notable absence of standardized components, parameters, and related considerations, such as problem characteristics and algorithm configurations. This lack of standardization introduces difficulties in the selection of valid component combinations and in the re -use of algorithmic configurations between different algorithm implementations. MOODY offers a means to infuse semantic annotations into the configurations found by automatic tools, enabling efficient querying of the results and seamless integration across diverse sources through their incorporation into a knowledge graph. We validate our proposal by presenting four case studies.
In this paper, we describe FFEAT - a library for GPU-based implementation of evolutionary algorithms based on Torch. We discuss limitations of GPU computing and how they affect implementations of evolutionary algorith...
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ISBN:
(纸本)9781450392686
In this paper, we describe FFEAT - a library for GPU-based implementation of evolutionary algorithms based on Torch. We discuss limitations of GPU computing and how they affect implementations of evolutionary algorithms and other population-based heuristics. Using FFEAT, we implement a number of different types of nature inspired algorithms, including evolutionary algorithms, evolution strategies, and particle swarm optimization. We investigate the performance of such algorithms in a number of benchmarks and with varying algorithm settings. We showthat in some cases, we can obtain an order of magnitude speed-up by running the algorithm on a GPU compared to running it on a CPU.
The mutation is one of the operators that is used by many evolutionary algorithms (EA) to diversify the population (solutions). It can enhance the algorithm exploration of the problem search space and improve the evol...
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ISBN:
(纸本)9781665474849;9781665474832
The mutation is one of the operators that is used by many evolutionary algorithms (EA) to diversify the population (solutions). It can enhance the algorithm exploration of the problem search space and improve the evolution process. This paper introduces a novel mutation technique that is based on a recently investigated mutation bias pattern in the Arabidopsis thaliana plant [1]. The proposed mutation technique is called an essential mutation. The proposed method uses the. parameter to control the amount of distance we can be from the parent's fitness. Three different configurations are studied and the best results are obtained when epsilon=0. It is compared against five well-known mutation techniques which are Boundary, Non-uniform, MPT, and Polynomial on standard benchmark functions. The obtained results show the superiority of the proposed essential mutation in terms of best solution and convergence speed in most of the test functions.
Finite Element Tearing and Interconnecting (FETI) methods are used in the engineering community to solve extremely large engineering simulations on clusters and supercomputers with thousands of computational nodes. Th...
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Finite Element Tearing and Interconnecting (FETI) methods are used in the engineering community to solve extremely large engineering simulations on clusters and supercomputers with thousands of computational nodes. This paper focuses on minimizing the execution time of these methods by searching for an optimal configuration with swarm and evolutionary algorithms (SEAs). It incorporates optimization of an expensive cost function (taking tens to hundreds of seconds) of discrete search space with up to hundreds of thousands of combinations constrained by its boundaries and incompatible individuals (invalid combination of FETI solver parameters). In addition, the optimization occurs in real time, i.e., during a simulation. Hence, the number of objective function evaluations must remain low (tens to lower hundreds). The paper compares the performance of 3 basic SEAs with a reduced population size (Differential Evolution, Particle Swarm Optimization, and Self- Organizing Migrating Algorithm), 4 micro SEAs (Micro Differential Evolution Ray, Improved Micro-Particle Swarm Optimization, Micro-Particle Swarm Optimization, and Micro-Genetic Algorithm), and a random search on 4 distinct time-dependent simulations of a heat transfer. The experiments show that basic SEAs with a small population (about 5 individuals) and a penalty system as protection against incompatible configurations represent the most effective solution. One can use them to find the optimal configuration of FETI-based methods in approximately 130 evaluations. It can improve the utilization of expensive hardware resources of modern computational clusters.
Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate ...
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ISBN:
(纸本)9781450392686
Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate a gradient. In this work, we study results obtained from using quantum-enhanced operators within the selection mechanism of a genetic algorithm. Our approach frames the selection process as a minimization of a binary quadratic model with which we encode fitness and distance between members of a population, and we leverage a quantum annealing system to sample low energy solutions for the selection mechanism. We benchmark these quantum-enhanced algorithms against classical algorithms over various black-box objective functions, including the OneMax function, and functions from the IOHProfiler library for black-box optimization. We observe a performance gain in average number of generations to convergence for the quantum-enhanced elitist selection operator in comparison to classical on the OneMax function. We also find that the quantum-enhanced selection operator with non-elitist selection outperform benchmarks on functions with fitness perturbation from the IOHProfiler library. Additionally, we find that in the case of elitist selection, the quantum-enhanced operators outperform classical benchmarks on functions with varying degrees of dummy variables and neutrality.
A fixed number of brush strokes images are initialized on a canvas, their position, size, rotation, colour, stroke type and drawing index all randomly chosen. These attributes are then modified by stochastic hillClimb...
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ISBN:
(纸本)9783031037894;9783031037887
A fixed number of brush strokes images are initialized on a canvas, their position, size, rotation, colour, stroke type and drawing index all randomly chosen. These attributes are then modified by stochastic hillClimbing, simulated annealing or the plant propagation algorithm, approximating a target image ever closer. Simulated annealing showed the best performance, followed by hill-Climbing;the plant propagation algorithm performed worst. Finally, the distribution of the attributes of the brush strokes shows us that there appears to be a preference for smaller brush strokes, and strokes of the fourth type.
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