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.
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.
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.
Inference of the biochemical systems (BSs) via experimental data is important for understanding how biochemical components in vivo interact with each other. However, it is not a trivial task because BSs usually functi...
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Inference of the biochemical systems (BSs) via experimental data is important for understanding how biochemical components in vivo interact with each other. However, it is not a trivial task because BSs usually function with complex and nonlinear dynamics. As a popular ordinary equation (ODE) model, the S-System describes the dynamical properties of BSs by incorporating the power rule of biochemical reactions but behaves as a challenge because it has a lot of parameters to be confirmed. This work is dedicated to proposing a general method for inference of S-Systems by experimental data, using a biobjective optimization (BOO) model and a specially mixed-variable multiobjective evolutionary algorithm (mv-MOEA). Regarding that BSs are sparse in common sense, we introduce binary variables indicating network connections to eliminate the difficulty of threshold presetting and take data fitting error and the L-0-norm as two objectives to be minimized in the BOO model. Then, a selection procedure that automatically runs tradeoff between two objectives is employed to choose final inference results from the obtained nondominated solutions of the mv-MOEA. Inference results of the investigated networks demonstrate that our method can identify their dynamical properties well, although the automatic selection procedure sometimes ignores some weak connections in BSs.
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.
Bilevel and multi-objective optimization methods are often useful to spatially target agri-environmental policy throughout a watershed. This type of problem is complex and is comprised of a number of practicalities: (...
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ISBN:
(纸本)9781450349208
Bilevel and multi-objective optimization methods are often useful to spatially target agri-environmental policy throughout a watershed. This type of problem is complex and is comprised of a number of practicalities: (i) a large number of decision variables, (ii) at least two inter-dependent levels of optimization between policy makers and policy followers, and (iii) uncertainty in decision variables and problem parameters. Given agricultural and economic data from the Raccoon watershed in central Iowa, we formulate a bilevel multi-objective optimization problem that accommodates objectives of both policy makers and farmers. The solution procedure then explicitly accounts for the nested nature of farm-level management decisions in response to agri-environmental policy incentives constructed by policy makers. We specifically examine the spatial targeting of a fertilizer-reduction incentive policy while seeking to maximize farm-level productivity while generating mandated water quality improvements using this framework. We test three different evolutionary optimization algorithms - m-BLEAQ, NSGA-II, and SPEA2 and show that m-BLEAQ is well suited for handling the bilevel optimization problems and the considered practicalities.
The availability of a model to measure the performance of evolutionary algorithms is very important, especially when these algorithms are applied to solve problems with high computational requirements. That model woul...
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The availability of a model to measure the performance of evolutionary algorithms is very important, especially when these algorithms are applied to solve problems with high computational requirements. That model would compute an index of the quality of the solution reached by the algorithm as a function of run-time. Conversely, if we fix an index of quality for the solution, the model would give the number of iterations to be expected. In this work, we develop a statistical model to describe the performance of PBIL and CHC evolutionary algorithms applied to solve the root identification problem. This problem is basic in constraint-based, geometric parametric modeling, as an instance of general constraint-satisfaction problems. The performance model is empirically validated over a benchmark with very large search spaces.
evolutionary algorithms are optimization methods inspired by natural evolution. They usually search for the optimal solution in large space areas. In evolutionary algorithms it is very important to select an appropria...
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ISBN:
(纸本)9788394625375
evolutionary algorithms are optimization methods inspired by natural evolution. They usually search for the optimal solution in large space areas. In evolutionary algorithms it is very important to select an appropriate balance between the ability of the algorithm to explore and exploit the search space. The paper presents a hybrid system consisting of a Genetic Algorithm and an evolutionary Strategy designed to optimize the function of many variables. In this system, we combined the ability of the Genetic Algorithm to explore the search space and the ability of the evolutionary Strategy to exploit the search space. Optimization performed by the Genetic Algorithm and the evolutionary Strategy runs at the same time, so it is possible to perform parallel computations. The results of the experiments suggest that the proposed system can be an effective tool in solving complex optimization problems.
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