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16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Friess, Stephen Tino, Peter Menzel, Stefan Sendhoff, Bernhard Yao, Xin Univ Birmingham Sch Comp Sci CERCIA Birmingham W Midlands England Honda Res Inst Europe Carl Legien Str 30 D-63073 Offenbach Germany Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9783030581114;9783030581121

The notion of learning from different problem instances, although an old and known one, has in recent years regained popularity within the optimization community. Notable endeavors have been drawing inspiration from machine learning methods as a means for algorithm selection and solution transfer. However, surprisingly approaches which are centered around internal sampling models have not been revisited. Even though notable algorithms have been established in the last decades. In this work, we progress along this direction by investigating a method that allows us to learn an evolutionary search strategy reflecting rough characteristics of a fitness landscape. This latter model of a search strategy is represented through a flexible mixture-based distribution, which can subsequently be transferred and adapted for similar problems of interest. We validate this approach in two series of experiments in which we first demonstrate the efficacy of the recovered distributions and subsequently investigate the transfer with a systematic from the literature to generate benchmarking scenarios.

关键词： Evolution strategies Model-based optimisation Continuous optimisation algorithm configuration Transfer learning

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On Adaptive Specialisation in Genetic Improvement 19

On Adaptive Specialisation in Genetic Improvement

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Blot, Aymeric Petke, Justyna UCL London England

ISBN: (纸本)9781450367486

Genetic improvement uses automated search to find improved versions of existing software. Software can either be evolved with general-purpose intentions or with a focus on a specific application (e.g., to improve it's efficiency for a particular class of problems). Unfortunately, software specialisation to each problem application is generally performed independently, fragmenting and slowing down an already very time-consuming search process. We propose to incorporate specialisation as an online mechanism of the general search process, in an attempt to automatically devise application classes, by benefiting from past execution history.

关键词： Genetic Improvement Search-Based Software Engineering Software Specialisation algorithm Selection algorithm configuration

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Characterization of Neighborhood Behaviours in a Multi-neighborhood Local Search algorithm 10th

Characterization of Neighborhood Behaviours in a Multi-neigh...

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10th International Conference on Learning and Intelligent Optimization (LION)

作者： Nguyen Thi Thanh Dang De Causmaecker, Patrick KU Leuven KULAK CODeS iMinds ITEC Kortrijk Belgium

ISBN: (纸本)9783319503493;9783319503486

We consider a multi-neighborhood local search framework with a large number of possible neighborhoods. Each neighborhood is accompanied by a weight value which represents the probability of being chosen at each iteration. These weights are fixed before the algorithm runs, and can be tuned by off-the-shelf off-line automated algorithm configuration tools (e.g., SMAC). However, the large number of parameters might deteriorate the tuning tool's efficiency, especially in our case where each run of the algorithm is not computationally cheap, even when the number of parameters has been reduced by some intuition. In this work, we propose a systematic method to characterize each neighborhood's behaviours, representing them as a feature vector, and using cluster analysis to form similar groups of neighborhoods. The novelty of our characterization method is the ability of reflecting changes of behaviours according to hardness of different solution quality regions based on simple statistics collected during any algorithm runs. We show that using neighborhood clusters instead of individual neighborhoods helps to reduce the parameter configuration space without misleading the search of the tuning procedure. Moreover, this method is problem-independent and potentially can be applied in similar contexts.

关键词： algorithm configuration Clustering Multi-neighborhood local search

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Non-elitist Selection Can Improve the Performance of Irace 17th

Non-elitist Selection Can Improve the Performance of Irace

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

作者： Ye, Furong Vermetten, Diederick Doerr, Carola Back, Thomas Leiden Univ LIACS Leiden Netherlands Sorbonne Univ CNRS LIP6 Paris France

ISBN: (纸本)9783031147142;9783031147135

Modern optimization strategies such as evolutionary algorithms, ant colony algorithms, Bayesian optimization techniques, etc. come with several parameters that steer their behavior during the optimization process. To obtain high-performing algorithm instances, automated algorithm configuration techniques have been developed. One of the most popular tools is irace, which evaluates configurations in sequential races, making use of iterated statistical tests to discard poorly performing configurations. At the end of the race, a set of elite configurations are selected from those survivor configurations that were not discarded, using greedy truncation selection. We study two alternative selection methods: one keeps the best survivor and selects the remaining configurations uniformly at random from the set of survivors, while the other applies entropy to maximize the diversity of the elites. These methods are tested for tuning ant colony optimization algorithms for traveling salesperson problems and the quadratic assignment problem and tuning an exact tree search solver for satisfiability problems. The experimental results show improvement on the tested benchmarks compared to the default selection of irace. In addition, the obtained results indicate that non-elitist can obtain diverse algorithm configurations, which encourages us to explore a wider range of solutions to understand the behavior of algorithms.

关键词： Parameter tuning algorithm configuration Black-box optimization Evolutionary computation

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Archiver Effects on the Performance of State-of-the-art Multi- and Many-objective Evolutionary algorithms

Archiver Effects on the Performance of State-of-the-art Mult...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Bezerra, Leonardo C. T. Lopez-Ibanez, Manuel Stutzle, Thomas Univ Fed Rio Grande do Norte Inst Metropole Digital Natal RN Brazil Univ Manchester Alliance Manchester Business Sch Manchester Lancs England Univ Libre Bruxelles CoDE IRIDIA Brussels Belgium

ISBN: (纸本)9781450361118

Early works on external solution archiving have pointed out the benefits of unbounded archivers and there have been great advances, theoretical and algorithmic, in bounded archiving methods. Moreover, recent work has shown that the populations of most multi- and many-objective evolutionary algorithms (MOEAs) lack the properties that one would desire when trying to find a bounded Pareto-optimal front. Despite all these results, many recent MOEAs are still being proposed, analyzed and compared without considering any kind of archiver assuming their additional computational cost is not justified. In this paper, we investigate the effect of using various kinds of archivers, improving over previous studies in several aspects: (i) the parameters of MOEAs with and without an external archiver are tuned separately using automatic configuration methods;(ii) we consider a comprehensive range of problem scenarios (number of objectives, function evaluations, computation time limit);(iii) we employ multiple, complementary quality metrics;and (iv) we study the effect of unbounded archivers and two state-of-the-art bounded archiving methods. Our results show that both unbounded and bounded archivers are beneficial even for many-objective problems. We conclude that future proposals and comparisons of MOEAs must include archiving as an algorithmic component.

关键词： Multi-objective optimization evolutionary algorithms algorithm configuration experimental analysis archiving

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Dancing to the State of the Art? How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem 18th

Dancing to the State of the Art? How Candidate Lists Influen...

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

作者： Heins, Jonathan Schaepermeier, Lennart Kerschke, Pascal Whitley, Darrell Tech Univ Dresden Big Data Analyt Transportat Dresden Germany ScaDS AI Dresden Leipzig Dresden Germany Colorado State Univ Dept Comp Sci Ft Collins CO USA

ISBN: (纸本)9783031700545;9783031700552

Solving the Traveling Salesperson Problem (TSP) remains a persistent challenge, despite its fundamental role in numerous generalized applications in modern contexts. Heuristic solvers address the demand for finding high-quality solutions efficiently. Among these solvers, the Lin-Kernighan-Helsgaun (LKH) heuristic stands out, as it complements the performance of genetic algorithms across a diverse range of problem instances. However, frequent timeouts on challenging instances hinder the practical applicability of the solver. Within this work, we investigate a previously overlooked factor contributing to many timeouts: The use of a fixed candidate set based on a tree structure. Our investigations reveal that candidate sets based on Hamiltonian circuits contain more optimal edges. We thus propose to integrate this promising initialization strategy, in the form of POPMUSIC, within an efficient restart version of LKH. As confirmed by our experimental studies, this refined TSP heuristic is much more efficient - causing fewer timeouts and improving the performance (in terms of penalized average runtime) by an order of magnitude - and thereby challenges the state of the art in TSP solving.

关键词： Traveling Salesperson Problem Heuristic Search Problem Hardness algorithm configuration Benchmarking

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Automatic parameter tuning for functional regionalization methods

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PAPERS IN REGIONAL SCIENCE 2017年第4期96卷 859-+页

作者： Manuel Casado-Diaz, Jose Martinez-Bernabeu, Lucas Florez-Revuelta, Francisco Univ Alicante Inst Int Econ POB 99 E-03080 Alicante Spain Univ Alicante Dept Appl Econ Anal POB 99 E-03080 Alicante Spain Kingston Univ Fac Sci Engn & Comp Penrhyn Rd Kingston Upon Thames KT1 2EE Surrey England

The methods used to define functional regions for public statistics and policy purposes need to establish several parameter values. This is typically achieved using expert knowledge based on qualitative judgements and lengthy consultations with local stakeholders. We propose to support this process by using an optimization algorithm to calibrate any regionalization method by identifying the parameter values that produce the best regionalization for a given quantitative indicator. The approach is exemplified by using a grid search and a genetic algorithm to configure the official methods employed in the UK and Sweden for the definition of their respective official concepts of local labour markets.

关键词： Labour market areas functional regions parameter tuning regionalization travel-to-work areas algorithm configuration

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Predicting CMA-ES Operators as Inductive Biases for Shape Optimization Problems

Predicting CMA-ES Operators as Inductive Biases for Shape Op...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Friess, Stephen Tino, Peter Menzel, Stefan Sendhoff, Bernhard Yao, Xin Univ Birmingham Sch Comp Sci CERCIA Birmingham W Midlands England Honda Res Inst Europe Offenbach Germany Southern Univ Sci & Technol Shenzhen Peoples R China

ISBN: (纸本)9781728190488

Domain-dependent expertise knowledge and high-level abstractions to arbitrate between different problem domains can be considered to be essential components of how human problem-solvers build experience and reuse it over the course of their lifetime. However, replicating it from an algorithmic point of view is a less trivial endeavor. Existing knowledge transfer methods in optimization largely fail to provide more specific guidance on specifying the similarity of different optimization problems and the nature of complementary experiences formed on them. A more rigorously grounded approach can be found alternatively in metalearning. This notion neglects any hurdles on characterizing problem similarity in favor of focusing instead on methodology to form domain-dependent inductive biases and mechanisms to arbitrate between them. In principle, we proposed within our previous research methods for constructing inductive biases and predict these from procedural optimization data. However, while we obtained effective methodology, it does not allow the joint construction of predictive components and biases in a cohesive manner. We therefore show in our following study, that improved configurations can be derived for the CMA-ES algorithm which can serve as inductive biases, and that predictors can be trained to recall them. Particularly noteworthy, this scenario allows the construction of predictive component and bias iteratively in a joint manner. We demonstrate the efficacy of this approach in a shape optimization scenario, in which the inductive bias is predicted through an operator configuration in a problem-specific manner during run-time.

关键词： Inductive Biases algorithm configuration Operator Representation Design Optimization

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A New Approach Towards the Combined algorithm Selection and Hyper-parameter Optimization Problem

A New Approach Towards the Combined Algorithm Selection and ...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Guo, Xin van Stein, Bas Back, Thomas Leiden Univ LIACS Leiden Netherlands

ISBN: (纸本)9781728124858

Machine learning algorithms often have many hyper-parameters that can be tuned to improve empirical performance. However, manually exploring the complex search spaces is tedious and cannot guarantee to find satisfactory outcomes. Recently, the Efficient Global Optimization (EGO) algorithm for solving the hyper-parameter optimization problem have shown substantial improvements. However, these algorithms cannot easily be expanded to include the selection of an algorithm. Automatically determining and optimizing an algorithm is known as the combined algorithm selection and hyper-parameter optimization (CASH) problem. In this paper, a novel mixed integer efficient global optimization algorithm and its variants are proposed to solve the CASH problem efficiently. The proposed algorithm is compared with a wide set of state-of-the-art algorithms in both a Black Box Optimization and a CASH problem setting. For the CASH problem setup, seven machine learning algorithms are optimized for a large set of classification t asks. Results show that the proposed algorithms outperform on the black box optimization task and can also outperform alternative state-of-the-art approaches on the CASH problem for specific instances.

关键词： algorithm configuration automatic machine learning bayesian optimization

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Learning Step-Size Adaptation in CMA-ES 16th

Learning Step-Size Adaptation in CMA-ES

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16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Shala, Gresa Biedenkapp, Andre Awad, Noor Adriaensen, Steven Lindauer, Marius Hutter, Frank Univ Freiburg Freiburg Germany Leibniz Univ Hannover Hannover Germany Bosch Ctr Artificial Intelligence Renningen Germany

ISBN: (纸本)9783030581114;9783030581121

An algorithm's parameter setting often affects its ability to solve a given problem, e.g., population-size, mutation-rate or crossover-rate of an evolutionary algorithm. Furthermore, some parameters have to be adjusted dynamically, such as lowering the mutation-strength over time. While hand-crafted heuristics offer a way to fine-tune and dynamically configure these parameters, their design is tedious, time-consuming and typically involves analyzing the algorithm's behavior on simple problems that may not be representative for those that arise in practice. In this paper, we show that formulating dynamic algorithm configuration as a reinforcement learning problem allows us to automatically learn policies that can dynamically configure the mutation step-size parameter of Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We evaluate our approach on a wide range of black-box optimization problems, and show that (i) learning step-size policies has the potential to improve the performance of CMA-ES;(ii) learned step-size policies can outperform the default Cumulative Step-Size Adaptation of CMA-ES;and transferring the policies to (iii) different function classes and to (iv) higher dimensions is also possible.

关键词： Evolutionary algorithms Reinforcement learning algorithm configuration

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