2,099 research outputs found
Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms
The problem of parameterization is often central to the effective deployment
of nature-inspired algorithms. However, finding the optimal set of parameter
values for a combination of problem instance and solution method is highly
challenging, and few concrete guidelines exist on how and when such tuning may
be performed. Previous work tends to either focus on a specific algorithm or
use benchmark problems, and both of these restrictions limit the applicability
of any findings. Here, we examine a number of different algorithms, and study
them in a "problem agnostic" fashion (i.e., one that is not tied to specific
instances) by considering their performance on fitness landscapes with varying
characteristics. Using this approach, we make a number of observations on which
algorithms may (or may not) benefit from tuning, and in which specific
circumstances.Comment: 8 pages, 7 figures. Accepted at the European Conference on Artificial
Life (ECAL) 2013, Taormina, Ital
A new sequential covering strategy for inducing classification rules with ant colony algorithms
Ant colony optimization (ACO) algorithms have been successfully applied to discover a list of classification rules. In general, these algorithms follow a sequential covering strategy, where a single rule is discovered at each iteration of the algorithm in order to build a list of rules. The sequential covering strategy has the drawback of not coping with the problem of rule interaction, i.e., the outcome of a rule affects the rules that can be discovered subsequently since the search space is modified due to the removal of examples covered by previous rules. This paper proposes a new sequential covering strategy for ACO classification algorithms to mitigate the problem of rule interaction, where the order of the rules is implicitly encoded as pheromone values and the search is guided by the quality of a candidate list of rules. Our experiments using 18 publicly available data sets show that the predictive accuracy obtained by a new ACO classification algorithm implementing the proposed sequential covering strategy is statistically significantly higher than the predictive accuracy of state-of-the-art rule induction classification algorithms
Population-based Algorithm Portfolios with automated constituent algorithms selection
AbstractPopulation-based Algorithm Portfolios (PAP) is an appealing framework for integrating different Evolutionary Algorithms (EAs) to solve challenging numerical optimization problems. Particularly, PAP has shown significant advantages to single EAs when a number of problems need to be solved simultaneously. Previous investigation on PAP reveals that choosing appropriate constituent algorithms is crucial to the success of PAP. However, no method has been developed for this purpose. In this paper, an extended version of PAP, namely PAP based on Estimated Performance Matrix (EPM-PAP) is proposed. EPM-PAP is equipped with a novel constituent algorithms selection module, which is based on the EPM of each candidate EAs. Empirical studies demonstrate that the EPM-based selection method can successfully identify appropriate constituent EAs, and thus EPM-PAP outperformed all single EAs considered in this work
Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO
The design of spacecraft trajectories for missions visiting multiple
celestial bodies is here framed as a multi-objective bilevel optimization
problem. A comparative study is performed to assess the performance of
different Beam Search algorithms at tackling the combinatorial problem of
finding the ideal sequence of bodies. Special focus is placed on the
development of a new hybridization between Beam Search and the Population-based
Ant Colony Optimization algorithm. An experimental evaluation shows all
algorithms achieving exceptional performance on a hard benchmark problem. It is
found that a properly tuned deterministic Beam Search always outperforms the
remaining variants. Beam P-ACO, however, demonstrates lower parameter
sensitivity, while offering superior worst-case performance. Being an anytime
algorithm, it is then found to be the preferable choice for certain practical
applications.Comment: Code available at https://github.com/lfsimoes/beam_paco__gtoc
Deep learning for video game playing
In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Most machine learning algorithms are configured by a set of hyperparameters whose values must be carefully chosen and which often considerably impact performance. To avoid a time-consuming and irreproducible manual process of trial-and-error to find well-performing hyperparameter configurations, various automatic hyperparameter optimization (HPO) methods—for example, based on resampling error estimation for supervised machine learning—can be employed. After introducing HPO from a general perspective, this paper reviews important HPO methods, from simple techniques such as grid or random search to more advanced methods like evolution strategies, Bayesian optimization, Hyperband, and racing. This work gives practical recommendations regarding important choices to be made when conducting HPO, including the HPO algorithms themselves, performance evaluation, how to combine HPO with machine learning pipelines, runtime improvements, and parallelization. This article is categorized under: Algorithmic Development > Statistics Technologies > Machine Learning Technologies > Prediction
Generation and optimisation of real-world static and dynamic location-allocation problems with application to the telecommunications industry.
The location-allocation (LA) problem concerns the location of facilities and the allocation of demand, to minimise or maximise a particular function such as cost, profit or a measure of distance. Many formulations of LA problems have been presented in the literature to capture and study the unique aspects of real-world problems. However, some real-world aspects, such as resilience, are still lacking in the literature. Resilience ensures uninterrupted supply of demand and enhances the quality of service. Due to changes in population shift, market size, and the economic and labour markets - which often cause demand to be stochastic - a reasonable LA problem formulation should consider some aspect of future uncertainties. Almost all LA problem formulations in the literature that capture some aspect of future uncertainties fall in the domain of dynamic optimisation problems, where new facilities are located every time the environment changes. However, considering the substantial cost associated with locating a new facility, it becomes infeasible to locate facilities each time the environment changes. In this study, we propose and investigate variations of LA problem formulations. Firstly, we develop and study new LA formulations, which extend the location of facilities and the allocation of demand to add a layer of resilience. We apply the population-based incremental learning algorithm for the first time in the literature to solve the new novel LA formulations. Secondly, we propose and study a new dynamic formulation of the LA problem where facilities are opened once at the start of a defined period and are expected to be satisfactory in servicing customers' demands irrespective of changes in customer distribution. The problem is based on the idea that customers will change locations over a defined period and that these changes have to be taken into account when establishing facilities to service changing customers' distributions. Thirdly, we employ a simulation-based optimisation approach to tackle the new dynamic formulation. Owing to the high computational costs associated with simulation-based optimisation, we investigate the concept of Racing, an approach used in model selection, to reduce the high computational cost by employing the minimum number of simulations for solution selection
Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in Neuroevolution
Although automated test generation is common in many programming domains,
games still challenge test generators due to their heavy randomisation and
hard-to-reach program states. Neuroevolution combined with search-based
software testing principles has been shown to be a promising approach for
testing games, but the co-evolutionary search for optimal network topologies
and weights involves unreasonably long search durations. In this paper, we aim
to improve the evolutionary search for game input generators by integrating
knowledge about human gameplay behaviour. To this end, we propose a novel way
of systematically recording human gameplay traces, and integrating these traces
into the evolutionary search for networks using traditional gradient descent as
a mutation operator. Experiments conducted on eight diverse Scratch games
demonstrate that the proposed approach reduces the required search time from
five hours down to only 52 minutes
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