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model-based evolutionary algorithms: a short survey

COMPLEX & INTELLIGENT SYSTEMS 2018年第4期4卷 283-292页

作者： Cheng, Ran He, Cheng Jin, Yaochu Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen 518055 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Univ Birmingham Sch Comp Sci Ctr Excellence Res Computat Intelligence & Applic Birmingham B15 2TT W Midlands England

The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are unable to learn the structures or properties of the problems to be optimized. To equip the EAs with learning abilities, recently, various model-based evolutionary algorithms (MBEAs) have been proposed. This survey briefly reviews some representative MBEAs by considering three different motivations of using models. First, the most commonly seen motivation of using models is to estimate the distribution of the candidate solutions. Second, in evolutionary multi-objective optimization, one motivation of using models is to build the inverse models from the objective space to the decision space. Third, when solving computationally expensive problems, models can be used as surrogates of the fitness functions. based on the review, some further discussions are also given.

关键词： model-based evolutionary algorithms Estimation of distribution algorithms Surrogate modelling Inverse modelling

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Introducing the Use of model-based evolutionary algorithms for EEG-based Motor Imagery Classification 12

Introducing the Use of Model-Based Evolutionary Algorithms f...

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

作者： Santana, Roberto Bonnet, Laurent Legeny, Jozef Lecuyer, Anatole Univ Basque Country UPV EHU Intelligent Syst Grp Bilbao Spain

ISBN: (纸本)9781450311779

Brain computer interfaces (BCIs) allow the direct human-computer interaction without the need of motor intervention. To properly and efficiently decode brain signals into computer commands the application of machine-learning techniques is required. evolutionary algorithms have been increasingly applied in different steps of BCI implementations. In this paper we introduce the use of the covariance matrix adaptation evolution strategy (CMA-ES) for BCI systems based on motor imagery. The optimization algorithm is used to evolve linear classifiers able to outperform other traditional classifiers. We also analyze the role of modeling variables interactions for additional insight in the understanding of the BCI paradigms.

关键词： brain computer interface model-based evolutionary algorithms CMA-ES optimization motor-imagery

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Parameterless Gene-Pool Optimal Mixing evolutionary algorithms

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evolutionary COMPUTATION 2024年第4期32卷 371-397页

作者： Dushatskiy, Arkadiy Virgolin, Marco Bouter, Anton Thierens, Dirk Bosman, Peter A. N. Ctr Wiskunde & Informat Evolutionary Intelligence Grp NL-1098XG Amsterdam Netherlands Univ Utrecht Dept Informat & Comp NL-3584CC Utrecht Netherlands Delft Univ Technol Dept Software Technol NL-2628XE Delft Netherlands

When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, that is, dependencies between variables, can be key. In this paper, we present the latest version of, and propose substantial enhancements to, the gene-pool optimal mixing evolutionary algorithm (GOMEA): an EA explicitly designed to estimate and exploit linkage information. We begin by performing a large-scale search over several GOMEA design choices to understand what matters most and obtain a generally best-performing version of the algorithm. Next, we introduce a novel version of GOMEA, called CGOMEA, where linkage-based variation is further improved by filtering solution mating based on conditional dependencies. We compare our latest version of GOMEA, the newly introduced CGOMEA, and another contending linkage-aware EA, DSMGA-II, in an extensive experimental evaluation, involving a benchmark set of nine black-box problems that can be solved efficiently only if their inherent dependency structure is unveiled and exploited. Finally, in an attempt to make EAs more usable and resilient to parameter choices, we investigate the performance of different automatic population management schemes for GOMEA and CGOMEA, de facto making the EAs parameterless. Our results show that GOMEA and CGOMEA significantly outperform the original GOMEA and DSMGA-II on most problems, setting a new state of the art for the field

关键词： model-based evolutionary algorithms linkage learning optimal mixing estimation-of-distribution algorithms genetic algorithms

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Coefficient Mutation in the Gene-pool Optimal Mixing evolutionary Algorithm for Symbolic Regression 22

Coefficient Mutation in the Gene-pool Optimal Mixing Evoluti...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Virgolin, Marco Bosman, Peter A. N. Ctr Wiskunde & Informat Amsterdam Netherlands

ISBN: (纸本)9781450392686

Currently, the genetic programming version of the gene-pool optimal mixing evolutionary algorithm (GP-GOMEA) is among the top-performing algorithms for symbolic regression (SR). A key strength of GP-GOMEA is its way of performing variation, which dynamically adapts to the emergence of patterns in the population. However, GP-GOMEA lacks a mechanism to optimize coefficients. In this paper, we study how fairly simple approaches for optimizing coefficients can be integrated into GP-GOMEA. In particular, we considered two variants of Gaussian coefficient mutation. We performed experiments using different settings on 23 benchmark problems, and used machine learning to estimate what aspects of coefficient mutation matter most. We find that the most important aspect is that the number of coefficient mutation attempts needs to be commensurate with the number of mixing operations that GP-GOMEA performs. We applied GP-GOMEA with the best-performing coefficient mutation approach to the data sets of SR-Bench, a large SR benchmark, for which a ground-truth underlying equation is known. We find that coefficient mutation can help rediscovering the underlying equation by a substantial amount, but only when no noise is added to the target variable. In the presence of noise, GP-GOMEA with coefficient mutation discovers alternative but similarly-accurate equations.

关键词： genetic programming symbolic regression coefficient optimization model-based evolutionary algorithms interpretable machine learning

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Envisioning the Benefits of Back-Drive in evolutionary algorithms

Envisioning the Benefits of Back-Drive in Evolutionary Algor...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Garciarena, Unai Mendiburu, Alexander Santana, Roberto Univ Basque Country UPV EHU Intelligent Syst Grp Donostia San Sebastian Spain

ISBN: (纸本)9781728169293

Among the characteristics of traditional evolutionary algorithms governed by models, memory volatility is one of the most frequent. This is commonly due to the limitations of the models used to guide this kind of algorithms, which are generally very efficient when sampling, but tend to struggle when facing large amounts of data to represent. Neural networks are one type of model which conveniently thrives when facing vast amounts of data, and does not see its performance particularly worsened by large dimensionality. Several successful neural generative models, which could perfectly fit as a model for driving an evolutionary process are available in the literature. Whereas the behavior of these generative models in evolutionary algorithms has already been widely tested, other neural models -those intended for supervised learning- have not enjoyed that much attention from the research community. In this paper, we take one step forward in this direction, exploring the capacities and particularities of back-drive, a method that enables a neural model intended for regression to be used as a solution sampling model. In this context, by performing extensive research into the most influential aspects of the algorithm, we study the conditions which favor the performance of the back-drive algorithm as the sole guiding factor in an evolutionary approach.

关键词： Deep learning model-based evolutionary algorithms Back-drive

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Simultaneous model-based Evolution of Constants and Expression Structure in GP-GOMEA for Symbolic Regression 18th

Simultaneous Model-Based Evolution of Constants and Expressi...

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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Koch, Johannes Alderliesten, Tanja Bosman, Peter A. N. Ctr Wiskunde & Informat Amsterdam Netherlands Delft Univ Technol Delft Netherlands Leiden Univ Med Ctr Leiden Netherlands

ISBN: (纸本)9783031700545;9783031700552

Genetic programming (GP) approaches are among the state-of-the-art for symbolic regression, the task of constructing symbolic expressions that fit well with data. To find highly accurate symbolic expressions, both the expression structure and any contained real-valued constants, are important. GP-GOMEA, a modern model-based evolutionary algorithm, is one of the leading algorithms for finding accurate, yet compact expressions. Yet, GP-GOMEA does not perform dedicated constant optimization, but rather uses ephemeral random constants. Hence, the accuracy of GP-GOMEA may well still be improved upon by the incorporation of a constant optimization mechanism. Existing research into mixed discrete-continuous optimization with EAs has shown that a simultaneous and well-integrated approach to optimizing both discrete and continuous parts, leads to the best results on a variety of problems, especially when there are interactions between these parts. In this paper, we therefore propose a novel approach where constants in expressions are optimized at the same time as the expression structure by merging the real-valued variant of GOMEA with GP-GOMEA. The proposed approach is compared to other forms of handling constants in GP-GOMEA, and in the context of other commonly used techniques such as linear scaling, restarts, and constant tuning after GP optimization. Our results indicate that our novel approach generally performs best and confirms the importance of simultaneous constant optimization during evolution.

关键词： Genetic programming Constant optimization Symbolic regression model-based evolutionary algorithms

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The Impact of Asynchrony on Parallel model-based EAs 23

The Impact of Asynchrony on Parallel Model-Based EAs

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Genetic and evolutionary Computation Conference (GECCO)

作者： Guijt, Arthur Thierens, Dirk Alderliesten, Tanja Bosman, Peter A. N. Ctr Wiskunde & Informat Amsterdam Netherlands Univ Utrecht Utrecht Netherlands Leiden Univ Med Ctr Leiden Netherlands Delft Univ Technol Delft Netherlands

ISBN: (纸本)9798400701191

In a parallel EA one can strictly adhere to the generational clock, and wait for all evaluations in a generation to be done. However, this idle time limits the throughput of the algorithm and wastes computational resources. Alternatively, an EA can be made asynchronous parallel. However, EAs using classic recombination and selection operators (GAs) are known to suffer from an evaluation time bias, which also influences the performance of the approach. model-based evolutionary algorithms (MBEAs) are more scalable than classic GAs by virtue of capturing the structure of a problem in a model. If this model is learned through linkage learning based on the population, the learned model may also capture biases. Thus, if an asynchronous parallel MBEA is also affected by an evaluation time bias, this could result in learned models to be less suited to solving the problem, reducing performance. Therefore, in this work, we study the impact and presence of evaluation time biases on MBEAs in an asynchronous parallelization setting, and compare this to the biases in GAs. We find that a modern MBEA, GOMEA, is unaffected by evaluation time biases, while the more classical MBEA, ECGA, is affected, much like GAs are.

关键词： Genetic algorithms model-based evolutionary algorithms Linkage Learning Parallel algorithms Asynchronous algorithms

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