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检索条件"主题词=Estimation-of-distribution algorithms"

共 33 条记录，以下是21-30 订阅

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A Novel Population-based Multi-Objective CMA-ES and the Impact of Different Constraint Handling Techniques 14

A Novel Population-based Multi-Objective CMA-ES and the Impa...

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16th Genetic and Evolutionary Computation Conference (GECCO)

作者： Rodrigues, Silvio Bauer, Pavol Bosman, Peter A. N. Delft Univ Technol TUDelft Delft Netherlands CWI Amsterdam Netherlands

ISBN: (纸本)9781450326629

The Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is a well-known, state-of-the-art optimization algorithm for single-objective real-valued problems, especially in black-box settings. Although several extensions of CMAES to multi-objective (MO) optimization exist, no extension incorporates a key component of the most robust and general CMA-ES variant: the association of a population with each Gaussian distribution that drives optimization. To achieve this, we use a recently introduced framework for extending population-based algorithms from single- to multi-objective optimization. We compare, using six well-known benchmark problems, the performance of the newly constructed MO-CMA- ES with existing variants and with the estimation of distribution algorithm (EDA) known as iMAMaLGaM, that is also an instance of the framework, extending the single- objective EDA iAMaLGaM to MO. Results underline the advantages of being able to use populations. Because many real-world problems have constraints, we also study the use of four constraint-handling techniques. We find that CMA-ES is typically less robust to these techniques than iAMaLGaM. Moreover, whereas we could verify that a penalty method that was previously used in literature leads to fast convergence, we also find that it has a high risk of finding only nearly, but not entirely, feasible solutions. We therefore propose that other constraint-handling techniques should be preferred in general.

关键词： algorithms Performance Experimentation CMA-ES estimation-of-distribution algorithms Mixture distributions Multi-Objective Optimization

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Parameter-less Local Optimizer with Linkage Identification for Deterministic Order-k Decomposable Problems 11

Parameter-less Local Optimizer with Linkage Identification f...

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13th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Posik, Petr Vanicek, Stanislav Czech Tech Univ FEE Dept Cybernet Prague 16627 6 Czech Republic

ISBN: (纸本)9781450305570

A simple parameter-less local optimizer able to solve deterministic problems with building blocks of bounded order is proposed in this article. The algorithm is able to learn and use linkage information during the run. The algorithm is algorithmically simple, easy to implement and with the exception of termination condition, it is completely parameter-free-there is thus no need to tune the population size and other parameters to the problem at hand. An empirical comparison on 3 decomposable functions, each with uniformly scaled building blocks of size 5 and 8, was carried out. The algorithm exhibits quadratic scaling with the problem dimensionality, but the comparison with the extended compact genetic algorithm and Bayesian optimization algorithm shows that it needs lower or comparable number of fitness function evaluations on the majority of functions for the tested problem dimensionalities. The results also suggest that the efficiency of the local optimizer compared to both the estimation-of-distribution algorithms should be better for problem sizes under at least a few hundreds of bits.

关键词： Local optimization linkage learning evolutionary algorithms estimation-of-distribution algorithms

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Predetermined versus Learned Linkage Models 12

Predetermined versus Learned Linkage Models

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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Thierens, Dirk Bosman, Peter A. N. Univ Utrecht Inst Informat & Comp Sci NL-3508 TC Utrecht Netherlands

ISBN: (纸本)9781450311779

The linkage tree genetic algorithm (LTGA) learns, each generation, a linkage model by building a hierarchical cluster tree. The LTGA is an instance of the more general gene-pool optimal mixing evolutionary algorithm (GOMEA) that uses a family of subsets (FOS) linkage model. We compare the performance of the linkage model learning LTGA with several predetermined FOS linkage models applied by GOMEA. The predetermined models are matched to the underlying problem structure off our benchmark functions: onemax, deceptive trap functions, maximal overlapping nearest-neighbor NK-landscapes, and weighted MAXCUT problems. Although the a priori fixed models are specifically designed to capture the interactions between the problem variables, experimental results show that - for problems with intricate interaction structure - they are actually less efficient than LTGA that dynamically learns a hierarchical tree model. Some of these observations were unexpected and raise the question of what exactly is the optimal linkage structure for a given problem as used by GOMEA. In the case of the NK-problem a linkage model that is an accurate description of the underlying additively decomposable fitness structure is clearly not an optimal linkage model. Being able to rebuild the linkage model each generation has clear benefits above using fixed, predetermined linkage models.

关键词： Evolutionary Computation Genetic algorithms estimation-of-distribution algorithms Linkage Learning Optimal Mixing Algorithm

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Theoretical Study of Optimizing Rugged Landscapes with the cGA 17th

Theoretical Study of Optimizing Rugged Landscapes with the c...

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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Friedrich, Tobias Koetzing, Timo Neumann, Frank Radhakrishnan, Aishwarya Univ Potsdam Hasso Plattner Inst Potsdam Germany Univ Adelaide Sch Comp Sci Adelaide SA Australia

ISBN: (纸本)9783031147210;9783031147203

estimation of distribution algorithms (EDAs) provide a distribution-based approach for optimization which adapts its probability distribution during the run of the algorithm. We contribute to the theoretical understanding of EDAs and point out that their distribution approach makes them more suitable to deal with rugged fitness landscapes than classical local search algorithms. Concretely, we make the ONEMAX function rugged by adding noise to each fitness value. The cGA can nevertheless find solutions with n(1- epsilon) many ls, even for high variance of noise. In contrast to this, RLS and the (1+1) EA, with high probability, only find solutions with n(1/2 + o(1)) many 1s, even for noise with small variance.

关键词： estimation-of-distribution algorithms Compact genetic algorithm Random local search Evolutionary algorithms Run time analysis Theory

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Medium Step Sizes are Harmful for the Compact Genetic Algorithm 18

Medium Step Sizes are Harmful for the Compact Genetic Algori...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Lengler, Johannes Sudholt, Dirk Witt, Carsten Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Tech Univ Denmark DTU Compute Lyngby Denmark

ISBN: (纸本)9781450356183

We study the intricate dynamics of the Compact Genetic Algorithm (cGA) on ONEMAX, and how its performance depends on the step size 1/K, that determines how quickly decisions about promising bit values are fixed in the probabilistic model. It is known that cGA and UMDA, a related algorithm, run in expected time O(n log n) when the step size is just small enough (K = Theta(root n log n) to avoid wrong decisions being fixed. UMDA also shows the same performance in a very different regime (equivalent to K = Theta(log n) in the cGA) with much larger steps sizes, but for very different reasons: many wrong decisions are fixed initially, but then reverted efficiently. We show that step sizes in between these two optimal regimes are harmful as they yield larger runtimes: we prove a lower bound Omega(K(1/3)n + n log n) for the cGA on ONEMAX for K = O(root n/log(2) n). For K = Omega(log(3) n) the runtime increases with growing K before dropping again to O(K root n + nlogn) for K = Omega(root n log n). This suggests that the expected runtime for cGA is a bimodal function in K with two very different optimal regions and worse performance in between.

关键词： estimation-of-distribution algorithms compact genetic algorithm evolutionary algorithms running time analysis theory

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Update Strength in EDAs and ACO: How to Avoid Genetic Drift 16

Update Strength in EDAs and ACO: How to Avoid Genetic Drift

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Sudholt, Dirk Witt, Carsten Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Tech Univ Denmark DTU Compute Lyngby Denmark

ISBN: (纸本)9781450342063

We provide a rigorous runtime analysis concerning the update strength, a vital parameter in probabilistic model-building GAs such as the step size 1/K in the compact Genetic Algorithm (cGA) and the evaporation factor rho in ACO. While a large update strength is desirable for exploitation, there is a general trade-off : too strong updates can lead to genetic drift and poor performance. We demonstrate this trade-off for the cGA and a simple MMAS ACO algorithm on the OneMax function. More precisely, we obtain lower bounds on the expected runtime of Omega(K root n + n log n) and Omega(root n/rho + n log n), respectively, showing that the update strength should be limited to 1/K, rho = O (1/(root n log n)). In fact, choosing 1/K, rho similar to 1/(root n log n) both algorithms efficiently optimize OneMax in expected time O (n log n). Our analyses provide new insights into the stochastic behavior of probabilistic model-building GAs and propose new guidelines for setting the update strength in global optimization.

关键词： Ant Colony Optimization estimation-of-distribution algorithms Iteration-Best Update Genetic Drift Runtime Analysis Theory

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Linkage Neighbors, Optimal Mixing and Forced Improvements in Genetic algorithms 12

Linkage Neighbors, Optimal Mixing and Forced Improvements in...

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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Bosman, Peter A. N. Thierens, Dirk CWI NL-1090 GB Amsterdam Netherlands

ISBN: (纸本)9781450311779

Recently, the Linkage Tree Genetic Algorithm (LTGA) was introduced as one of the latest developments in a line of EA research that studies building models to capture and exploit linkage information between problem variables. LTGA was reported to exhibit excellent performance on several linkage benchmark problems, mainly attributed to use of the LT linkage model. In this paper we consider a technique called Forced Improvements (FI), that allows LTGA to converge to a single solution without requiring an explicit, diversity-reducing, selection step. We further consider a different linkage model, called Linkage Neighbors (LN), that is more flexible, yet can be learned equally efficiently from data. Even with the simplest learning approach for configuring the LN, better results are obtained on the linkage benchmark problems than when the LT model is used However, on weighted MAXCUT (a combinatorial optimization problem), very poor results are obtained and a more involved multiscale LN variant is required to obtain a performance near that of LTGA. Our results underline the advantage of processing linkage in a single model on multiple scales as well as the importance of also considering problems other problems than common linkage benchmark problems when judging the merits of linkage learning techniques.

关键词： Genetic algorithms estimation-of-distribution algorithms Linkage learning Optimal Mixing

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More Concise and Robust Linkage Learning by Filtering and Combining Linkage Hierarchies 13

More Concise and Robust Linkage Learning by Filtering and Co...

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15th Genetic and Evolutionary Computation Conference (GECCO)

作者： Bosman, Peter A. N. Thierens, Dirk CWI POB 94079 NL-1090 GB Amsterdam Netherlands Univ Utrecht Dept Comp Sci NL-3508 TB Utrecht Netherlands

ISBN: (纸本)9781450319638

Genepool Optimal Mixing Evolutionary algorithms (GOMEAs) were recently proposed as a new way of designing linkage-friendly, efficiently-scalable evolutionary algorithms (EAs). GOMEAs combine the building of linkage models with an intensive, greedy mixing procedure. Recent results indicate that the use of hierarchical linkage models in GOMEAs lead to the most robust and efficient performance. Two of such GOMEA instances are the Linkage Tree Genetic Algorithm (LTGA) and the Multi-scale Linkage Neighbors Genetic Algorithm (MLNGA). The linkage models in these GOMEAs have their individual merits and drawbacks. In this paper, we propose enhancement techniques targeted at filtering out superfluous linkage sets from hierarchical linkage models and we consider a way to construct a linkage model that combines the strengths of different linkage models. We then propose a new GOMEA instance, called the Linkage Trees and Neighbors Genetic Algorithm (LTNGA), that combines the models of LTGA and MLNGA. LTNGA performs comparable or better than the best of either LTGA or MLNGA on various problems, including typical linkage benchmark problems and instances of the well-known combinatorial problem MAXCUT, especially when the proposed filtering techniques are used.

关键词： Genetic algorithms estimation-of-distribution algorithms Linkage learning Optimal Mixing

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The Compact Genetic Algorithm Struggles on Cliff Functions

The Compact Genetic Algorithm Struggles on Cliff Functions

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Neumann, Frank Sudholt, Dirk Witt, Carsten Univ Adelaide Optimisat & Logist Sch Comp Sci Adelaide SA Australia Univ Passau Fac Comp Sci & Math Passau Germany Tech Univ Denmark DTU Compute Lyngby Denmark

ISBN: (纸本)9781450392372

The compact genetic algorithm (cGA) is a non-elitist estimation of distribution algorithm which has shown to be able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. In this paper, we investigate the cGA on the Cliff function for which it has been shown recently that non-elitist evolutionary algorithms and artificial immune systems optimize it in expected polynomial time. We point out that the cGA faces major difficulties when solving the Cliff function and investigate its dynamics both experimentally and theoretically around the Cliff. Our experimental results indicate that the cGA requires exponential time for all values of the update strength... We show theoretically that, under sensible assumptions, there is a negative drift when sampling around the location of the cliff. Experiments further suggest that there is a phase transition for.. where the expected optimization time drops from n Theta((n)) to 2 Theta((n)).

关键词： estimation-of-distribution algorithms compact genetic algorithm evolutionary algorithms running time analysis theory.

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Incremental Gaussian Model-Building in Multi-Objective EDAs with an Application to Deformable Image Registration 12

Incremental Gaussian Model-Building in Multi-Objective EDAs ...

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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Bosman, Peter A. N. Alderliesten, Tanja Centrum Wiskunde & Informat CWI NL-1090 GB Amsterdam Netherlands

ISBN: (纸本)9781450311779

estimation-of-distribution algorithms (EDAs) build and use probabilistic models during optimization in order to automatically discover and use an optimization problems' structure. This is especially useful for black-box optimization where no assumptions are made on the problem being solved, which is characteristic of many cases in solving complex real-world problems. In this paper we consider multi-objective optimization problems with real-valued variables. Although the vast majority of advances in EDA literature concern single-objective optimization, advances have also been made in multi-objective optimization. In this paper we bring together two recent advances, namely incremental Gaussian model building to reduce the required population size and a mixture-based multi-objective framework that has specific methods to better facilitate model-building techniques that span multiple generations. Significantly faster convergence to the optimal Pareto front is achieved on 6 out of 7 artificial benchmark problems from literature. Although results on two of these problems show that building models with higher-order interactions between variables is required, these problems are still artificial. We therefore also consider a more realistic optimization problem in image processing, namely deformable image registration. For this problem too, our results show the need for processing interactions between problem variables, stressing the importance of studying the use of such models. Furthermore, the number of problem variables in the deformable image registration problem can be very large. The building of models with higher-order interactions, especially mixture-based models, then requires very large population sizes. The use of incremental model building is therefore of high importance. This claim is supported by our results that show a huge reduction in the number of required evaluations on this problem.

关键词： Evolutionary algorithms estimation-of-distribution algorithms Multi-Objective Optimization Incremental Model Building Deformable Image Registration

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