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Extremal optimization

About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.


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Proceedings ArticleDOI
01 Jan 2020
TL;DR: This paper explores the influence maximization problem for the weighted cascade model by considering an approach based on Shapley value and Extremal Optimization, and results reported are promising, prompting for further research in this direction.
Abstract: This paper explores the influence maximization problem for the weighted cascade model by considering an approach based on Shapley value and Extremal Optimization. The Shapley value is a solution concept in cooperative game theory that, given a total value of the game assigns to each player a value as part of it, computed as its marginal contribution to all possible coalitions of players. In the weighted cascade model we consider adding and updating nodes in the initial set during the extremal optimization search based on their Shapley value in an approach already tested for the independent cascade model. Comparisons with other methods by means of numerical experiments show that results reported by this approach are promising, prompting for further research in this direction.
Proceedings ArticleDOI
30 Dec 2010
TL;DR: The experimental results show that the modified EO algorithms provide better performances than the original one and further support the observation that power-law is not the only good evolutionary distribution in EO, others such as exponential and hybrid distributions may be better choices.
Abstract: Finding the ground states of Sherrington-Kirkpatrick (SK) spin glass, the mean-filed spin glass model with strongly connected variables, is well known as a typical NP-hard problem. This paper presents a modified extremal optimization (EO) framework to approximate its grounds states. The basic idea behind the proposed framework is to generalize the evolutionary probability distribution of the original EO algorithm. The experimental results show that the modified EO algorithms provide better performances than the original one and further support the observation that power-law is not the only good evolutionary distribution in EO, others such as exponential and hybrid distributions may be better choices.
Proceedings ArticleDOI
05 Jul 2020
TL;DR: The experiments have shown that the multi-objective EO approach included into the load balancing algorithms visibly improves the quality of program execution.
Abstract: Multi-objective algorithms based on nature-inspired approach of Extremal optimization (EO) used in distributed processor load balancing have been studied in the paper. EO defines task migration aiming at processor load balancing in execution of graph-represented distributed programs. In the multi-objective EO approach, three objectives relevant to distributed processor load balancing are simultaneously controlled: the function dealing with the computational load imbalance in execution of application tasks on processors, the function concerned with the communication between tasks placed on distinct computing nodes and the function related to the task migration number. An important aspect of the proposed multiobjective approach is the method for selecting the best solutions from the Pareto set. Pareto front analysis based on compromise solution approach, lexicographic approach and hybrid approach (lexicographic + numerical threshold) has been performed in dependence on the program graph features, the executive system characteristics and the experimental setting. The algorithms are assessed by simulation experiments with macro data flow graphs of programs run in distributed systems. The experiments have shown that the multi-objective EO approach included into the load balancing algorithms visibly improves the quality of program execution.
Book ChapterDOI
08 Sep 1975
TL;DR: It was found that SICOBA could be used mainly for the three following purposes: testing various strategies on various combinatorial optimization problems, helping to find better heuristics and for solving directly complex problems without using mathematical models.
Abstract: It was found that SICOBA could be used mainly for the three following purposes for testing various strategies on various combinatorial optimization problems for helping to find better heuristics for solving directly complex problems without using mathematical models.
DOI
01 Jan 2016
TL;DR: Diversity Optimization and Parameterized Analysis of Heuristic Search Methods for Combinatorial Optimization problems and their applications in reinforcement learning and reinforcement learning are studied.
Abstract: Faculty of Engineering, Computer & Mathematical Science School of Computer Science Doctor of Philosophy Diversity Optimization and Parameterized Analysis of Heuristic Search Methods for Combinatorial Optimization Problems

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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20232
202213
20217
20209
201922
201815