403 research outputs found
A visual demonstration of convergence properties of cooperative coevolution
We introduce a model for cooperative coevolutionary algorithms (CCEAs) using partial mixing, which allows us to compute the expected long-run convergence of such algorithms when individuals ’ fitness is based on the maximum payoff of some N evaluations with partners chosen at random from the other population. Using this model, we devise novel visualization mechanisms to attempt to qualitatively explain a difficult-to-conceptualize pathology in CCEAs: the tendency for them to converge to suboptimal Nash equilibria. We further demonstrate visually how increasing the size of N, or biasing the fitness to include an ideal-collaboration factor, both improve the likelihood of optimal convergence, and under which initial population configurations they are not much help
Multi-Guide Particle Swarm Optimization for Large-Scale Multi-Objective Optimization Problems
Multi-guide particle swarm optimization (MGPSO) is a novel metaheuristic for multi-objective optimization based on particle swarm optimization (PSO). MGPSO has been shown to be competitive when compared with other state-of-the-art multi-objective optimization algorithms for low-dimensional problems. However, to the best of the author’s knowledge, the suitability of MGPSO for high-dimensional multi-objective optimization problems has not been studied. One goal of this thesis is to provide a scalability study of MGPSO in order to evaluate its efficacy for high-dimensional multi-objective optimization problems. It is observed that while MGPSO has comparable performance to state-of-the-art multi-objective optimization algorithms, it experiences a performance drop with the increase in the problem dimensionality. Therefore, a main contribution of this work is a new scalable MGPSO-based algorithm, termed cooperative co-evolutionary multi-guide particle swarm optimization (CCMGPSO), that incorporates ideas from cooperative PSOs. A detailed empirical study on well-known benchmark problems comparing the proposed improved approach with various state-of-the-art multi-objective optimization algorithms is done. Results show that the proposed CCMGPSO is highly competitive for high-dimensional problems
Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction with Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces
© 2012 IEEE. The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analysis and related learning systems, which has attracted increasing attention from both the scientific community and industry. In this paper, we propose a shared nearest-neighbor quantum game-based attribute reduction (SNNQGAR) algorithm that incorporates the hierarchical coevolutionary Spark model. We first present a shared coevolutionary nearest-neighbor hierarchy with self-evolving compensation that considers the features of nearest-neighborhood attribute subsets and calculates the similarity between attribute subsets according to the shared neighbor information of attribute sample points. We then present a novel attribute weight tensor model to generate ranking vectors of attributes and apply them to balance the relative contributions of different neighborhood attribute subsets. To optimize the model, we propose an embedded quantum equilibrium game paradigm (QEGP) to ensure that noisy attributes do not degrade the big data reduction results. A combination of the hierarchical coevolutionary Spark model and an improved MapReduce framework is then constructed that it can better parallelize the SNNQGAR to efficiently determine the preferred reduction solutions of the distributed attribute subsets. The experimental comparisons demonstrate the superior performance of the SNNQGAR, which outperforms most of the state-of-the-art attribute reduction algorithms. Moreover, the results indicate that the SNNQGAR can be successfully applied to segment overlapping and interdependent fuzzy cerebral tissues, and it exhibits a stable and consistent segmentation performance for neonatal cerebral cortical surfaces
Bandit-based cooperative coevolution for tackling contribution imbalance in large-scale optimization problems
This paper addresses the issue of computational resource allocation within the context of cooperative coevolution. Cooperative coevolution typically works by breaking a problem down into smaller subproblems (or components) and coevolving them in a round-robin fashion, resulting in a uniform resource allocation among its components. Despite its success on a wide range of problems, cooperative coevolution struggles to perform efficiently when its components do not contribute equally to the overall objective value. This is of crucial importance on large-scale optimization problems where such difference are further magnified. To resolve this imbalance problem, we extend the standard cooperative coevolution to a new generic framework capable of learning the contribution of each component using multi-armed bandit techniques. The new framework allocates the computational resources to each component proportional to their contributions towards improving the overall objective value. This approach results in a more economical use of the limited computational resources. We study different aspects of the proposed framework in the light of extensive experiments. Our empirical results confirm that even a simple bandit-based credit assignment scheme can significantly improve the performance of cooperative coevolution on large-scale continuous problems, leading to competitive performance as compared to the state-of-the-art algorithms
Novelty-driven cooperative coevolution
Cooperative coevolutionary algorithms (CCEAs) rely on multiple coevolving populations for the evolution of solutions composed of coadapted components. CCEAs enable, for instance, the evolution of cooperative multiagent systems composed of heterogeneous agents, where each agent is modelled as a component of the solution. Previous works have, however, shown that CCEAs are biased toward stability: the evolutionary process tends to converge prematurely to stable states instead of (near-)optimal solutions. In this study, we show how novelty search can be used to avoid the counterproductive attraction to stable states in coevolution. Novelty search is an evolutionary technique that drives evolution toward behavioural novelty and diversity rather than exclusively pursuing a static objective. We evaluate three novelty-based approaches that rely on, respectively (1) the novelty of the team as a whole, (2) the novelty of the agents’ individual behaviour, and (3) the combination of the two. We compare the proposed approaches with traditional fitness-driven cooperative coevolution in three simulated multirobot tasks. Our results show that team-level novelty scoring is the most effective approach, significantly outperforming fitness-driven coevolution at multiple levels. Novelty-driven cooperative coevolution can substantially increase the potential of CCEAs while maintaining a computational complexity that scales well with the number of populations.info:eu-repo/semantics/publishedVersio
DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization
Identification of variable interaction is essential for an efficient implementation of a divide-and-conquer algorithm for large-scale black-box optimization. In this paper, we propose an improved variant of the differential grouping (DG) algorithm, which has a better efficiency and grouping accuracy. The proposed algorithm, DG2, finds a reliable threshold value by estimating the magnitude of roundoff errors. With respect to efficiency, DG2 reuses the sample points that are generated for detecting interactions and saves up to half of the computational resources on fully separable functions. We mathematically show that the new sampling technique achieves the lower bound with respect to the number of function evaluations. Unlike its predecessor, DG2 checks all possible pairs of variables for interactions and has the capacity to identify overlapping components of an objective function. On the accuracy aspect, DG2 outperforms the state-of-the-art decomposition methods on the latest large-scale continuous optimization benchmark suites. DG2 also performs reliably in the presence of imbalance among contribution of components in an objective function. Another major advantage of DG2 is the automatic calculation of its threshold parameter ( ), which makes it parameter-free. Finally, the experimental results show that when DG2 is used within a cooperative co-evolutionary framework, it can generate competitive results as compared to several state-of-the-art algorithms
Vector Autoregressive Evolution for Dynamic Multi-Objective Optimisation
Dynamic multi-objective optimisation (DMO) handles optimisation problems with
multiple (often conflicting) objectives in varying environments. Such problems
pose various challenges to evolutionary algorithms, which have popularly been
used to solve complex optimisation problems, due to their dynamic nature and
resource restrictions in changing environments. This paper proposes vector
autoregressive evolution (VARE) consisting of vector autoregression (VAR) and
environment-aware hypermutation to address environmental changes in DMO. VARE
builds a VAR model that considers mutual relationship between decision
variables to effectively predict the moving solutions in dynamic environments.
Additionally, VARE introduces EAH to address the blindness of existing
hypermutation strategies in increasing population diversity in dynamic
scenarios where predictive approaches are unsuitable. A seamless integration of
VAR and EAH in an environment-adaptive manner makes VARE effective to handle a
wide range of dynamic environments and competitive with several popular DMO
algorithms, as demonstrated in extensive experimental studies. Specially, the
proposed algorithm is computationally 50 times faster than two widely-used
algorithms (i.e., TrDMOEA and MOEA/D-SVR) while producing significantly better
results
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Theoretical and implementation improvements for difference evaluation functions
Multiagent learning with cooperative coevolutionary algorithms is a critical area of research, and is relevant to many real-world applications including air traffic control, distributed sensor network control, and game-theoretic applications such as border patrol. A key difficulty in multiagent learning is the credit assignment problem, where the impact of each individual agent on the overall system performance must be ascertained. Difference evaluation functions aim to solve this credit assignment problem, by approximating the effect that each agent has on the system evaluation function. Difference evaluations have proven to produce superior learned policies in many multiagent settings.
Although difference evaluations have produced excellent empirical results, there are still three key research questions that must be addressed regarding their usefulness in real-world systems. More specifically, the performance, theoretical advantages, and methodology for implementation must be addressed in order to demonstrate that difference evaluations are practical for use in real-world multiagent learning. These research questions are addressed in this dissertation. The first contribution of this dissertation is to demonstrate that difference evaluations may be extended and combined with other coordination mechanisms, resulting in superior learned performance. The second contribution of this dissertation is to derive conditions which guarantee that difference evaluations will outperform traditional coordination mechanisms. The third and final contribution of this dissertation is to demonstrate that difference evaluations may be approximated using only local knowledge, allowing for their implementation in any generic multiagent learning setting. By addressing the performance, theoretical foundation, and implementation concerns of difference evaluations, this dissertation provides a detailed analysis demonstrating the usefulness of difference evaluation functions in multiagent learning systems
A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems
Copyright @ Springer-Verlag 2008Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant Nos. 70431003 and 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, and the National Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01
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