Biological networks are structurally adaptive and take on non-random topological properties that influence system robustness. Studies are only beginning to reveal how these structural features emerge, however the infl...
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Biological networks are structurally adaptive and take on non-random topological properties that influence system robustness. Studies are only beginning to reveal how these structural features emerge, however the influence of component fitness and community cohesion (modularity) have attracted interest from the scientific community. In this study, we apply these concepts to an evolutionary algorithm and allow its population to self-organize using information that the population receives as it moves over a fitness landscape. More precisely, we employ fitness and clustering based topological operators for guiding network structural dynamics, which in turn are guided by population changes taking place over evolutionary time. To investigate the effect on evolution, experiments are conducted on six engineering design problems and six artificial test functions and compared against cellular genetic algorithms and panmictic evolutionary algorithm designs. Our results suggest that a self-organizing topology evolutionary algorithm can exhibit robust search behavior with strong performance observed over short and long time scales. More generally, the coevolution between a population and its topology may constitute a promising new paradigm for designing adaptive search heuristics.
Previous running time analyses of evolutionary algorithms (EAs) in noisy environments often studied the one-bit noise model, which flips a randomly chosen bit of a solution before evaluation. In this paper, we study a...
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ISBN:
(纸本)9781450349208
Previous running time analyses of evolutionary algorithms (EAs) in noisy environments often studied the one-bit noise model, which flips a randomly chosen bit of a solution before evaluation. In this paper, we study a natural extension of one-bit noise, the bit-wise noise model, which independently flips each bit of a solution with some probability. We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds. The analysis on LeadingOnes under bit-wise noise can be easily transferred to one-bit noise, and improves the previously known results.
Increasing information transmission in public networks raises a significant number of questions. For example, the security, the confidentiality, the integrity and the authenticity of the data during its transmission a...
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Increasing information transmission in public networks raises a significant number of questions. For example, the security, the confidentiality, the integrity and the authenticity of the data during its transmission are very problematical. So, encryption of the transmitted data is one of the most promising solutions. In our work, we focus on the security of image data, which are considered as specific data because of their big size and their information which are of two-dimensional nature and also redundant. These data characteristics make the developed algorithms in the literature unavailable in their classical forms, because of the speed and the possible risk of information loss. In this paper, we develop an original "images encryption'' algorithm based on evolutionary algorithms. The appropriateness of the proposed scheme is demonstrated by the sensitivity to images, the key and the resistibility to various advanced attacks.
There is an increasing interest in the application of evolutionary algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very ...
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There is an increasing interest in the application of evolutionary algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.
Robust design optimization (RDO) seeks to find optimal designs which are less sensitive to the uncontrollable variations that are often inherent to the design process. Studies using evolutionary algorithms (EAs) for R...
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Robust design optimization (RDO) seeks to find optimal designs which are less sensitive to the uncontrollable variations that are often inherent to the design process. Studies using evolutionary algorithms (EAs) for RDO are not too many. In this work, we propose enhancements to an EA based robust optimization procedure with explicit function evaluation saving strategies. The proposed algorithm, IDEAR, takes into account a specified expected uncertainty in the design variables and then imposes the desired robustness criteria during the optimization process to converge to robust optimal solution(s). We pick up a number of Bi-objective engineering design problems from the standard literature and study them in the proposed robust optimization framework to demonstrate the enhanced performance. A cross-validation study is performed to analyze whether the solutions obtained are truly robust and also make some observations on how robust optimal solutions differ from the performance maximizing solutions in the design space. We perform a rigorous analysis of the key features of IDEAR to illustrate its functioning. The proposed function evaluation saving strategies are generic and their applications are worth exploring in other areas of computational design optimization. [DOI: 10.1115/1.4004807]
With the continuous advancement of industry 4.0, also in the area of production and logistics optimization, a more holistic consideration of problems is required. Therefore, in contrary to the traditional sequential o...
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ISBN:
(纸本)9781450349390
With the continuous advancement of industry 4.0, also in the area of production and logistics optimization, a more holistic consideration of problems is required. Therefore, in contrary to the traditional sequential optimization approach in the area of operations research, in this paper, an integrated solution approach called optimization networks (ON) is presented. In an ON, multiple problem models get connected and optimized by an evolutionary solution algorithm. By having several optimization runs, in which the results of the preceding optimizations are considered, opportunity costs which could arise out of a traditional sequential optimization approach, are avoided.
In this paper a novel evolutionary algorithm for optimal positioning of wind turbines in wind farms is proposed. A realistic model for the wind farm is considered in the optimization process, which includes orography,...
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In this paper a novel evolutionary algorithm for optimal positioning of wind turbines in wind farms is proposed. A realistic model for the wind farm is considered in the optimization process, which includes orography, shape of the wind farm, simulation of the wind speed and direction, and costs of installation, connection and road construction among wind turbines. Regarding the solution of the problem, this paper introduces a greedy heuristic algorithm which is able to obtain a reasonable initial solution for the problem. This heuristic is then used to seed the initial population of the evolutionary algorithm, improving its performance. It is shown that the proposed seeded evolutionary approach is able to obtain very good solutions to this problem, which maximize the economical benefit which can be obtained from the wind farm. (C) 2011 Elsevier Ltd. All rights reserved.
Hybridizing evolutionary algorithms with local search has become a popular trend in recent years. There is empirical evidence for various combinatorial problems where hybrid evolutionary algorithms perform better than...
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Hybridizing evolutionary algorithms with local search has become a popular trend in recent years. There is empirical evidence for various combinatorial problems where hybrid evolutionary algorithms perform better than plain evolutionary algorithms. Due to the rapid development of a highly active field of research, theory lags far behind and a solid theoretical foundation of hybrid metaheuristics is sorely needed. We are aiming at a theoretical understanding of why and when hybrid evolutionary algorithms are successful in combinatorial optimization. To this end, we consider a hybrid of a simple evolutionary algorithm, the (1+1) EA, with a powerful local search operator known as variable-depth search (VDS) or Kernighan-Lin. Three combinatorial problems are investigated: Mincut, Knapsack, and Maxsat. More precisely, we focus on simply structured problem instances that contain local optima which are very hard to overcome for many common metaheuristics. The plain (1+1) EA, iterated local search, and simulated annealing need exponential time for optimization, with high probability. In sharp contrast, the hybrid algorithm using VDS finds a global optimum in expected polynomial time. These results demonstrate the usefulness of hybrid evolutionary algorithms with VDS from a rigorous theoretical perspective.
We present an approach to diversity maintenance based on separating the population into buckets based on similarity and biasing selection to keep individuals from all buckets in the population. We look at two approach...
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ISBN:
(纸本)9781450349390
We present an approach to diversity maintenance based on separating the population into buckets based on similarity and biasing selection to keep individuals from all buckets in the population. We look at two approaches to bucketing. The first uses a locally sensitive bucketing function on individuals. The second uses the K-Means clustering algorithms to divide the population. We focus our research on a family of deceptive problem domains which we dub Tricky Keys and analyze how the using bucketing methods changes evolutionary search results for problem instances of varying difficulty. Our results show that both bucketing by function and bucketing by clustering methods show an increase in probability of finding a good solution and in number of good solutions found.
Procedural Content Generation (PCG) for Games is a field that in the past few years has seen both extensive academic study and practical use in the games industry. One of its common uses being the generation of maps f...
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ISBN:
(纸本)9781538648469
Procedural Content Generation (PCG) for Games is a field that in the past few years has seen both extensive academic study and practical use in the games industry. One of its common uses being the generation of maps for levels within games that rely on replayability. While Cellular Automata is a PCG technique widely used for the creation of minor graphical systems, it has not yet seen much practical use in the generation of levels, part due to its inherent stochastic nature. With the purpose of presenting a malleable approach for improving levels created through Cellular Automata, this work presents a methodology that guides the generation process through the use of fractals, specifically Space-filling Curves. The product Automata of this process are implemented and polished on the Unity game engine, as to present their potential for generating procedural levels. Results show that this methodology can be used for the generation of organic, cohesive game levels.
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