312 research outputs found
On Selection of a Benchmark by Determining the Algorithms' Qualities
ABSTRACT: The authors got the motivation for writing the article based on an issue, with which developers of the newly developed nature-inspired algorithms are usually confronted today: How to select the test benchmark such that it highlights the quality of the developed algorithm most fairly? In line with this, the CEC Competitions on Real-Parameter Single-Objective Optimization benchmarks that were issued several times in the last decade, serve as a testbed for evaluating the collection of nature-inspired algorithms selected in our study. Indeed, this article addresses two research questions: (1) How the selected benchmark affects the ranking of the particular algorithm, and (2) If it is possible to find the best algorithm capable of outperforming all the others on all the selected benchmarks. Ten outstanding algorithms (also winners of particular competitions) from different periods in the last decade were collected and applied to benchmarks issued during the same time period. A comparative analysis showed that there is a strong correlation between the rankings of the algorithms and the benchmarks used, although some deviations arose in ranking the best algorithms. The possible reasons for these deviations were exposed and commented on.This work was supported in part by the Slovenian Research Agency (Projects J2-1731 and L7-9421) under Grant P2-0041, in part by the Project PDE-GIR of the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie under Grant 778035, and in part by the Spanish Ministry of Science, Innovation and Universities (Computer Science National Program) of the Agencia Estatal de Investigacion and European Funds EFRD (AEI/FEDER, UE) under Grant TIN2017–89275-R
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
In many technical fields, single-objective optimization procedures in
continuous domains involve expensive numerical simulations. In this context, an
improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial
super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide
fast convergence speed, high solution accuracy and robust performance over a
wide range of problems. It implements enhancements of the ABC structure and
hybridizations with interpolation strategies. The latter are inspired by the
quadratic trust region approach for local investigation and by an efficient
global optimizer for separable problems. Each modification and their combined
effects are studied with appropriate metrics on a numerical benchmark, which is
also used for comparing AsBeC with some effective ABC variants and other
derivative-free algorithms. In addition, the presented algorithm is validated
on two recent benchmarks adopted for competitions in international conferences.
Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc
Summarizing Strategy Card Game AI Competition
This paper concludes five years of AI competitions based on Legends of Code
and Magic (LOCM), a small Collectible Card Game (CCG), designed with the goal
of supporting research and algorithm development. The game was used in a number
of events, including Community Contests on the CodinGame platform, and Strategy
Card Game AI Competition at the IEEE Congress on Evolutionary Computation and
IEEE Conference on Games. LOCM has been used in a number of publications
related to areas such as game tree search algorithms, neural networks,
evaluation functions, and CCG deckbuilding. We present the rules of the game,
the history of organized competitions, and a listing of the participant and
their approaches, as well as some general advice on organizing AI competitions
for the research community. Although the COG 2022 edition was announced to be
the last one, the game remains available and can be played using an online
leaderboard arena
Genetic improvement of programs
Genetic programming can optimise software, including: evolving test benchmarks, generating hyper-heuristics by searching meta-heuristics, generating communication protocols, composing telephony systems and web services, generating improved hashing and C++ heap managers, redundant programming and even automatic bug fixing. Particularly in embedded real-time or mobile systems, there may be many ways to trade off expenses (such as time, memory, energy, power consumption) vs. Functionality. Human programmers cannot try them all. Also the best multi-objective Pareto trade off may change with time, underlying hardware and network connection or user behaviour. It may be GP can automatically suggest different trade offs for each new market. Recent results include substantial speed up by evolving a new version of a program customised for a special case
looking back and looking forward
Mcdermott, J., Kronberger, G., Orzechowski, P., Vanneschi, L., Manzoni, L., Kalkreuth, R., & Castelli, M. (2022). Genetic programming benchmarks: looking back and looking forward. ACM SIGEVOlution, 15(3), 1-19. https://doi.org/10.1145/3578482.3578483The top image shows a set of scales, which are intended to bring to mind the ideas of balance and fair experimentation which are the focus of our article on genetic programming benchmarks in this issue. Image by Elena Mozhvilo and made available under the Unsplash license on https://unsplash.com/photos/j06gLuKK0GM.authorsversionpublishe
Evolutionary Behavior Tree Approaches for Navigating Platform Games
Computer games are highly dynamic environments, where players are faced with a multitude of potentially unseen scenarios. In this article, AI controllers are applied to the Mario AI Benchmark platform, by using the Grammatical Evolution system to evolve Behavior Tree structures. These controllers are either evolved to both deal with navigation and reactiveness to elements of the game, or used in conjunction with a dynamic A* approach. The results obtained highlight the applicability of Behavior Trees as representations for evolutionary computation, and their flexibility for incorporation of diverse algorithms to deal with specific aspects of bot control in game environments
Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations
Given the ubiquity of non-separable optimization problems in real worlds, in
this paper we analyze and extend the large-scale version of the well-known
cooperative coevolution (CC), a divide-and-conquer optimization framework, on
non-separable functions. First, we reveal empirical reasons of why
decomposition-based methods are preferred or not in practice on some
non-separable large-scale problems, which have not been clearly pointed out in
many previous CC papers. Then, we formalize CC to a continuous game model via
simplification, but without losing its essential property. Different from
previous evolutionary game theory for CC, our new model provides a much simpler
but useful viewpoint to analyze its convergence, since only the pure Nash
equilibrium concept is needed and more general fitness landscapes can be
explicitly considered. Based on convergence analyses, we propose a hierarchical
decomposition strategy for better generalization, as for any decomposition
there is a risk of getting trapped into a suboptimal Nash equilibrium. Finally,
we use powerful distributed computing to accelerate it under the multi-level
learning framework, which combines the fine-tuning ability from decomposition
with the invariance property of CMA-ES. Experiments on a set of
high-dimensional functions validate both its search performance and scalability
(w.r.t. CPU cores) on a clustering computing platform with 400 CPU cores
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