We examine the effects of controller representation on the ability of an evolutionary algorithm to develop an open-loop solution to the variable speed turn-circle intercept problem. In this problem, two agents, an Att...
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ISBN:
(纸本)9781728121536
We examine the effects of controller representation on the ability of an evolutionary algorithm to develop an open-loop solution to the variable speed turn-circle intercept problem. In this problem, two agents, an Attacker and a Target, move about an obstacle free two-dimensional plane with equally bounded turn rates. The Target is restricted to turning with a constant velocity along a circle whereas the Attacker is able to move about the entire two-dimensional plane with variable speed and bounded turn rate. The Attacker attempts to maneuver into an advantageous position behind the Target on the Target's turn-circle in minimum time. We examine three different parameterization methods to represent the Attacker's control strategy. Each method adds or removes segments within the open loop controller in a different way, and we compare the resulting performance and evolutionary convergence time.
Running several evolutionary algorithms in parallel and occasionally exchanging good solutions is referred to as island models. The idea is that the independence of the different islands leads to diversity, thus possi...
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ISBN:
(纸本)9783319992594;9783319992587
Running several evolutionary algorithms in parallel and occasionally exchanging good solutions is referred to as island models. The idea is that the independence of the different islands leads to diversity, thus possibly exploring the search space better. Many theoretical analyses so far have found a complete (or sufficiently quickly expanding) topology as underlying migration graph most efficient for optimization, even though a quick dissemination of individuals leads to a loss of diversity. We suggest a simple fitness function FORK with two local optima parametrized by r >= 2 and a scheme for composite fitness functions. We show that, while the (1 + 1) EA gets stuck in a bad local optimum and incurs a run time of Theta(n(2r)) fitness evaluations on FORK, island models with a complete topology can achieve a run time of Theta(n(1.5r)) by making use of rare migrations in order to explore the search space more effectively. Finally, the ring topology, making use of rare migrations and a large diameter, can achieve a run time of (Theta) over tilde (n(r)), the black box complexity of FORK. This shows that the ring topology can be preferable over the complete topology in order to maintain diversity.
In this paper we address the problem of finding valid solutions for the problem of inferring gene regulatory networks. Different approaches to directly infer the dependencies of gene regulatory networks by identifying...
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ISBN:
(纸本)0780393635
In this paper we address the problem of finding valid solutions for the problem of inferring gene regulatory networks. Different approaches to directly infer the dependencies of gene regulatory networks by identifying parameters of mathematical models can be found in literature. The problem of reconstructing regulatory systems from experimental data is often multi-modal and thus appropriate optimization strategies become necessary. Thus, we propose to use a clustering based niching evolutionary algorithm to maintain diversity in the optimization population to prevent premature convergence and to raise the probability of finding the global optimum by identifying multiple alternative networks. With this set of alternatives, the identification of the true solution has then to be addressed in a second post-processing step.
Denoising autoencoder genetic programming (DAE-GP) is an estimation of distribution genetic programming (EDA-GP) algorithm. It uses denoising autoencoder long short-term memory networks as probabilistic model to repla...
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ISBN:
(纸本)9798400701207
Denoising autoencoder genetic programming (DAE-GP) is an estimation of distribution genetic programming (EDA-GP) algorithm. It uses denoising autoencoder long short-term memory networks as probabilistic model to replace the standard mutation and recombination operators of genetic programming (GP). Recent work has shown several advantages regarding solution length and overall performance of DAE-GP when compared to GP. However, training a neural network at each generation is computationally expensive, where model training is the most time consuming process of DAE-GP. In this work, we propose pretraining to reduce the runtime of the DAE-GP. In pretraining, the neural network is trained preceding the evolutionary search. In experiments on 8 real-world symbolic regression tasks we find that DAE-GP with pretraining has a reduced overall runtime of an order of magnitude while generating individuals with similar or better fitness.
This paper proposes a theoretical and experimental analysis of the expected running time for an elitist parallel evolutionary Algorithm (pEA) based on an island model executed over small-world networks. Our study assu...
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ISBN:
(纸本)9781479904549;9781479904532
This paper proposes a theoretical and experimental analysis of the expected running time for an elitist parallel evolutionary Algorithm (pEA) based on an island model executed over small-world networks. Our study assumes the resolution of optimization problems based on unimodal pseudo-boolean funtions. In particular, for such function with d values, we improve the previous asymptotic upper bound for the expected parallel running time from O(d root n) to O(d log n). This study is a first step towards the analysis of influence of more complex network topologies (like random graphs created by P2P networks) on the runtime of pEAs. A concrete implementation of the analysed algorithm have been performed on top of the ParadisEO framework and run on the HPC platform of the University of Luxembourg (UL). Our experiments confirm the expected speed-up demonstrated in this article and prove the benefit that pEA can gain from a small-world network topology.
This paper considers a pattern classification by the ensemble of evolutionary RBF networks. Mathematical models generally have a dilemma about model complexity, so the structure determination of RBF network can be con...
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ISBN:
(纸本)9788995003848
This paper considers a pattern classification by the ensemble of evolutionary RBF networks. Mathematical models generally have a dilemma about model complexity, so the structure determination of RBF network can be considered as the multi-objective optimization problem concerning with accuracy and complexity of the model. The set of RBF networks are obtained by multi-objective evolutionary computation, and then RBF network ensemble is constructed of all or some RBF networks at the final generation. Some experiments on the benchmark problem of the pattern classification demonstrate that the RBF network ensemble has comparable generalization ability to conventional ensemble methods.
Differential Evolution (DE) is an evolutionary algorithm (EA) known for its simplicity, robustness and performance. Compared to other EAs, DE has shown better performance according to recent research. In this paper a ...
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ISBN:
(纸本)9781479909957;9781479909971
Differential Evolution (DE) is an evolutionary algorithm (EA) known for its simplicity, robustness and performance. Compared to other EAs, DE has shown better performance according to recent research. In this paper a DE algorithm is designed for controller optimization of a PMDC motor speed regulation system. Presenting a comprehensive description of the plant, architecture is designed for automatic speed regulation. Then the DE algorithm is applied on the system to optimize the controller parameters. Performance of the optimal controller is studied with simulations and performance and robustness of the DE algorithm has been analyzed.
This paper introduces a method of combining Greg Hornby's Age Layered Protocol System with a form of spatial co-evolution. The combined system (SCALP) is compared to these two systems and a canonical GP tournament...
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ISBN:
(纸本)9781424481262
This paper introduces a method of combining Greg Hornby's Age Layered Protocol System with a form of spatial co-evolution. The combined system (SCALP) is compared to these two systems and a canonical GP tournament selection scheme over three well understood domains, the sextic regression problem, a two variable regression problem and a variation on the classic minesweeper problem. In each case SCALP avoided premature convergence;solving every run of these particular problems.
Continuous optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. Through CEC 2005 to CEC 2010 competitions, many different algorithms have been proposed to solve continuou...
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ISBN:
(纸本)9781424478354
Continuous optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. Through CEC 2005 to CEC 2010 competitions, many different algorithms have been proposed to solve continuous problems. The advances on this type of problems are of capital importance as many real-world problems from very different domains (biology, engineering, data mining, etc.) can be formulated as the optimization of a continuous function. For this reason, we have proposed a hybrid DE-RHC algorithm that combines the search strength of Differential Evolution with the explorative ability of a Random Hill Climber, which can help the Differential Evolution algorithm to reach new promising areas in difficult fitness landscapes, such as those than can be found on real-world problems. To evaluate this approach, the benchmark problems proposed in the "Testing evolutionary Algorithms on Real-world Numerical Optimization Problems" CEC 2011 special session have been considered.
This paper analyzes the behavior of the XCS classifier system on imbalanced datasets. We show that XCS with standard parameter settings is quite robust to considerable class imbalances. For high class imbalances, XCS ...
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ISBN:
(纸本)9781595931863
This paper analyzes the behavior of the XCS classifier system on imbalanced datasets. We show that XCS with standard parameter settings is quite robust to considerable class imbalances. For high class imbalances, XCS suffers from biases toward the majority class. We analyze XCS's behavior under such extreme imbalances and prove that appropriate parameter tuning improves significantly XCS's performance. Specifically, we counterbalance the imbalance ratio by equalizing the reproduction probabilities of the most occurring and least occurring niches. The study provides guidelines to tune XCS's parameters for unbalanced datasets, based on the dataset imbalance ratio. We propose a method to estimate the imbalance ratio during XCS's training and adapt XCS's parameters online.
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