In this paper, we describe the experience gained when evolutionary algorithms were applied to SATB music composition and the impact it is demonstrating in Spanish Professional Music Conservatories for the last three y...
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ISBN:
(纸本)9798400701207
In this paper, we describe the experience gained when evolutionary algorithms were applied to SATB music composition and the impact it is demonstrating in Spanish Professional Music Conservatories for the last three years. To our knowledge, this is the first time that an EA-inspired tool has been used at a national level in music conservatories.
This paper introduces the optimization of dynamic molecular alignment by shaped ferntosecond laser pulses, and analyzes the application of various evolutionary algorithms to this challenging real-life high-dimensional...
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ISBN:
(纸本)9780780394872
This paper introduces the optimization of dynamic molecular alignment by shaped ferntosecond laser pulses, and analyzes the application of various evolutionary algorithms to this challenging real-life high-dimensional physics problem. With an expensive simulator evaluation of 35 seconds, standard evolutionary approaches based on low-dimensional parameterizations of the electric field are applied to the task, compared among each other, and shown to be clearly inferior with respect to other methods. This numerical phase provides new insights into the problem, and is meant to be followed by a lab phase.
Cognitive radio (CR) technology employing dynamic spectrum access (DSA) improves spectrum utilization by exploiting its unused portions and provides a solution to the apparent spectrum scarcity problem. In this paper ...
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ISBN:
(纸本)9781424457373
Cognitive radio (CR) technology employing dynamic spectrum access (DSA) improves spectrum utilization by exploiting its unused portions and provides a solution to the apparent spectrum scarcity problem. In this paper we present binary particle swarm optimization (BPSO) and genetic algorithm (GA) for radio resource management (RRM) in OFDMA-based cognitive radio network (CRN). The simulation results show that BPSO-based RRM performs better than GA.
Designing a drug is the process of finding or creating a molecule which has a specific activity on a biological organism. Drug design is difficult since there are only few molecules that are both effective against a c...
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evolutionary algorithms have been widely used for a range of stochastic optimization problems in order to address complex real-world optimization problems. We consider the knapsack problem where the profits involve un...
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ISBN:
(纸本)9783031147142;9783031147135
evolutionary algorithms have been widely used for a range of stochastic optimization problems in order to address complex real-world optimization problems. We consider the knapsack problem where the profits involve uncertainties. Such a stochastic setting reflects important real-world scenarios where the profit that can be realized is uncertain. We introduce different ways of dealing with stochastic profits based on tail inequalities such as Chebyshev's inequality and Hoeffding bounds that allow to limit the impact of uncertainties. We examine simple evolutionary algorithms and the use of heavy tail mutation and a problem-specific crossover operator for optimizing uncertain profits. Our experimental investigations on different benchmarks instances show the results of different approaches based on tail inequalities as well as improvements achievable through heavy tail mutation and the problem specific crossover operator.
Phylogenetic inference of the history of life on Earth continues to be a major effort of evolutionary biology. Such inference can be accomplished through the use of individual genes, sets of genes, or complete genomes...
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ISBN:
(纸本)9781467315098
Phylogenetic inference of the history of life on Earth continues to be a major effort of evolutionary biology. Such inference can be accomplished through the use of individual genes, sets of genes, or complete genomes. While the latter may provide the most robust description of the true phylogenetic history, the computational demands of complete genome comparison and phylogenetic construction is daunting. Thus most researchers are left using sets of conserved genes for the resolution of a common phylogeny (what is termed a "supertree" search). However as the number of taxa increases or as the number of source trees used in construction of a supertree increases, the number of possible supertree solutions increases tremendously. This requires consideration of alternate methods to search this space efficiently such as those that use stochastic methods. Here for the first time we present a method for supertree search using evolutionary algorithms and evaluate its utility on a set of derived supertree problems with 50 taxa. The results indicate the utility of this approach and offer opportunities for future refinement.
Location area (LA) management is a very important problem in mobile networks. In general, registration and paging costs are associated with tracking the current location of a mobile user. Considering minimizing the to...
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ISBN:
(纸本)3540253963
Location area (LA) management is a very important problem in mobile networks. In general, registration and paging costs are associated with tracking the current location of a mobile user. Considering minimizing the total of paging and registration costs as the main objective, the aim is to provide corresponding cell-to-switch and cell-to-LA assignments. This paper compares three well-known evolutionary algorithms to measure their suitability for solving location area management problems;these are genetic algorithms, multi-population genetic algorithms and memetic algorithms. To handle multiple objectives of paging and registration, a two-stage multi-population CA is developed. A memetic algorithm is introduced in order to improve the performance of a CA with the local search techniques. The effectiveness of these methods is shown for a number of test problems with different network size and characteristics.
In this study we propose obstacle-aware evolutionary algorithms to identify optimised network topologies for electricity distribution networks including isolated microgrids or stand-alone power systems. We outline the...
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ISBN:
(数字)9781665467087
ISBN:
(纸本)9781665467087
In this study we propose obstacle-aware evolutionary algorithms to identify optimised network topologies for electricity distribution networks including isolated microgrids or stand-alone power systems. We outline the extension of two evolutionary algorithms that are modified to consider different types of geographically constrained areas in electricity distribution planning. These areas are represented as polygonal obstacles that either cannot be traversed or cause a higher weight factor when traversing. Both proposed evolutionary algorithms are extended such that they find optimised network solutions that avoid solid obstacles and consider the increased cost of traversing soft obstacles. The algorithms are tested and compared on different types of problem instances with solid and soft obstacles and the problem-specific evolutionary algorithm can be shown to successfully find low cost network topologies on a range of different test instances.
In this work we investigate the use of prediction mechanisms in evolutionary algorithms for dynamic environments. These mechanisms, linear regression and Markov chains are, used to estimate the generation when a chang...
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ISBN:
(纸本)9783540876991
In this work we investigate the use of prediction mechanisms in evolutionary algorithms for dynamic environments. These mechanisms, linear regression and Markov chains are, used to estimate the generation when a change in the environment will occur, and also to predict to which state (or states) the environment may change respectively. Different types of environmental changes were studied. A memory-based evolutionary algorithm empowered by these two techniques was successfully applied to several instances of the, dynamic bit, matching problem.
This work deals with the problem of automatically finding optimal partitions in bioinformatics datasets. We propose incremental improvements for a Clustering Genetic Algorithm (CGA), culminating in the evolutionary Al...
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ISBN:
(纸本)0769521428
This work deals with the problem of automatically finding optimal partitions in bioinformatics datasets. We propose incremental improvements for a Clustering Genetic Algorithm (CGA), culminating in the evolutionary Algorithm for Clustering (EAC). The CGA and its modified versions are evaluated in five gene-expression datasets, showing that the proposed EA C is a promising tool for clustering gene-expression data.
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