3,106 research outputs found
Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior
Previous research using evolutionary computation in Multi-Agent Systems
indicates that assigning fitness based on team vs.\ individual behavior has a
strong impact on the ability of evolved teams of artificial agents to exhibit
teamwork in challenging tasks. However, such research only made use of
single-objective evolution. In contrast, when a multiobjective evolutionary
algorithm is used, populations can be subject to individual-level objectives,
team-level objectives, or combinations of the two. This paper explores the
performance of cooperatively coevolved teams of agents controlled by artificial
neural networks subject to these types of objectives. Specifically, predator
agents are evolved to capture scripted prey agents in a torus-shaped grid
world. Because of the tension between individual and team behaviors, multiple
modes of behavior can be useful, and thus the effect of modular neural networks
is also explored. Results demonstrate that fitness rewarding individual
behavior is superior to fitness rewarding team behavior, despite being applied
to a cooperative task. However, the use of networks with multiple modules
allows predators to discover intelligent behavior, regardless of which type of
objectives are used
A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels
In this paper, a salient search and optimisation algorithm based on a new reduced space searching strategy, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a ‘real-life’ problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. Furthermore, this new algorithm is extended to the multi-objective optimisation case. Simulation results of optimising some challenging benchmark problems suggest that both the proposed single objective and multi-objective optimisation algorithms outperform some of the other well-known Evolutionary Algorithms (EAs). The proposed algorithms are further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of ‘right-first-time production’ of metals
The True Destination of EGO is Multi-local Optimization
Efficient global optimization is a popular algorithm for the optimization of
expensive multimodal black-box functions. One important reason for its
popularity is its theoretical foundation of global convergence. However, as the
budgets in expensive optimization are very small, the asymptotic properties
only play a minor role and the algorithm sometimes comes off badly in
experimental comparisons. Many alternative variants have therefore been
proposed over the years. In this work, we show experimentally that the
algorithm instead has its strength in a setting where multiple optima are to be
identified
Improved sampling of the pareto-front in multiobjective genetic optimizations by steady-state evolution: a Pareto converging genetic algorithm
Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we present a simple steady-state strategy, Pareto Converging Genetic Algorithm (PCGA), which naturally samples the solution space and ensures population advancement towards the Pareto-front. PCGA eliminates the need for sharing/niching and thus minimizes heuristically chosen parameters and procedures. A systematic approach based on histograms of rank is introduced for assessing convergence to the Pareto-front, which, by definition, is unknown in most real search problems.
We argue that there is always a certain inheritance of genetic material belonging to a population, and there is unlikely to be any significant gain beyond some point; a stopping criterion where terminating the computation is suggested. For further encouraging diversity and competition, a nonmigrating island model may optionally be used; this approach is particularly suited to many difficult (real-world) problems, which have a tendency to get stuck at (unknown) local minima. Results on three benchmark problems are presented and compared with those of earlier approaches. PCGA is found to produce diverse sampling of the Pareto-front without niching and with significantly less computational effort
Multimodal estimation of distribution algorithms
Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima
Generalized Hybrid Evolutionary Algorithm Framework with a Mutation Operator Requiring no Adaptation
This paper presents a generalized hybrid evolutionary optimization structure that not only combines both nondeterministic and deterministic algorithms on their individual merits and distinct advantages, but also offers behaviors of the three originating classes of evolutionary algorithms (EAs). In addition, a robust mutation operator is developed in place of the necessity of mutation adaptation, based on the mutation properties of binary-coded individuals in a genetic algorithm. The behaviour of this mutation operator is examined in full and its performance is compared with adaptive mutations. The results show that the new mutation operator outperforms adaptive mutation operators while reducing complications of extra adaptive parameters in an EA representation
Sub-structural Niching in Estimation of Distribution Algorithms
We propose a sub-structural niching method that fully exploits the problem
decomposition capability of linkage-learning methods such as the estimation of
distribution algorithms and concentrate on maintaining diversity at the
sub-structural level. The proposed method consists of three key components: (1)
Problem decomposition and sub-structure identification, (2) sub-structure
fitness estimation, and (3) sub-structural niche preservation. The
sub-structural niching method is compared to restricted tournament selection
(RTS)--a niching method used in hierarchical Bayesian optimization
algorithm--with special emphasis on sustained preservation of multiple global
solutions of a class of boundedly-difficult, additively-separable multimodal
problems. The results show that sub-structural niching successfully maintains
multiple global optima over large number of generations and does so with
significantly less population than RTS. Additionally, the market share of each
of the niche is much closer to the expected level in sub-structural niching
when compared to RTS
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