Agents: An undistorted representation of problem structure

Author(s):  
J. Yelon ◽  
L. V. Kalé
Keyword(s):  
2021 ◽  
Vol 1 (2) ◽  
pp. 1-23
Author(s):  
Arkadiy Dushatskiy ◽  
Tanja Alderliesten ◽  
Peter A. N. Bosman

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the domain of pseudo-Boolean functions in a black-box setting. Moreover, instead of using a surrogate model as an approximation of a fitness function, we propose to precisely learn the coefficients of the Walsh decomposition of a fitness function and use the Walsh decomposition as a surrogate. If the coefficients are learned correctly, then the Walsh decomposition values perfectly match with the fitness function, and, thus, the optimal solution to the problem can be found by optimizing the surrogate without any additional evaluations of the original fitness function. It is known that the Walsh coefficients can be efficiently learned for pseudo-Boolean functions with k -bounded epistasis and known problem structure. We propose to learn dependencies between variables first and, therefore, substantially reduce the number of Walsh coefficients to be calculated. After the accurate Walsh decomposition is obtained, the surrogate model is optimized using GOMEA, which is considered to be a state-of-the-art binary optimization algorithm. We compare the proposed approach with standard GOMEA and two other Walsh decomposition-based algorithms. The benchmark functions in the experiments are well-known trap functions, NK-landscapes, MaxCut, and MAX3SAT problems. The experimental results demonstrate that the proposed approach is scalable at the supposed complexity of O (ℓ log ℓ) function evaluations when the number of subfunctions is O (ℓ) and all subfunctions are k -bounded, outperforming all considered algorithms.


1984 ◽  
Vol 15 (2) ◽  
pp. 129-147 ◽  
Author(s):  
Alan Bell ◽  
Efraim Fischbein ◽  
Brian Greer

2002 ◽  
Vol 18 ◽  
pp. 797-802 ◽  
Author(s):  
Atsumi FURUYA ◽  
Izumi SEKI ◽  
Takuya MATSUMOTO ◽  
Akira NAGANO

Author(s):  
J. R. Jagannatha Rao ◽  
Panos Y. Papalambros

Abstract Decomposition strategies are used in a variety of practical design optimization applications. Such decompositions are valid, if the solution of the decomposed problem is in fact also the solution to the original one. Conditions for such validity are not always obvious. In the present article, we develop conditions for two-level parametric decomposition under which: (1) isolated minima at the two levels imply an isolated minimum for the original problem; (2) necessary conditions at the two-levels are equivalent to the necessary conditions for the original problem; and, (3) a descent algorithm for computing Karush-Kuhn-Tucker points in decomposition formulations is globally convergent. Since no special problem structure is assumed, the results are general and could be used to evaluate the suitability of a variety of approaches and algorithms for decomposition strategies.


2016 ◽  
Vol 7 (4) ◽  
pp. 16-26
Author(s):  
Uk Jung ◽  
Seongmin Yim ◽  
Sunguk Lim ◽  
Chongman Kim

AbstractAHP and the Kano model are such prevalent TQM tools that it may be surprising that a true hybrid decision-making model has so far eluded researchers. The quest for a hybrid approach is complicated by the differing output perspective of each model, namely discrete ranking (AHP) versus a multi-dimensional picture (Kano). This paper presents a hybrid model of AHP and Kano model, so called two-dimension AHP (2D-AHP).This paper first compares the two approaches and justifies a hybrid model based on a simple conceit drawn from the Kano perspective: given a decision hierarchy, child and parent elements can exhibit multi-dimension relationships under different circumstances. Based on this premise, the authors construct a hybrid two-dimension AHP model whereby a functional-dysfunctional question-pair technique is incorporated into a traditional AHP framework.Using the proposed hybrid model, this paper provides a practical test case of its implementation. The 2D-AHP approach revealed important evaluation variances obscured through AHP, while a survey study confirmed that the 2D-AHP approach is both feasible and preferred in some respects by respondents.Although there have been rich research efforts to combine AHP and Kano model, most of them is simply about a series of individual usage of each methodology. On the other hand, the type of hybridization between AHP and Kano model in this paper is quite unique in terms of the two dimensional perspective. The model provides a general approach with application possibilities far beyond the scope of the test case and its problem structure, and so calls for application and validation in new cases.


Covid-19 pandemic has created unprecedented interruption for the global business industry management. The world economy already facing a turbulent phase experienced the worst scenario in the view of this pandemic. Business management strategists and policymakers have been making an impact assessment to understand the problem structure of this worst possible pandemic situation. The present article tries to develop a viewpoint on Covid-19 impact on business industries and management. Further authors attempt to develop a problem-solving structure by discussing the best possible solutions to mitigate the fact on the one hand and facilitating the business process in various sectors such as business Industry, Marketing, finance, and health industries on the other.


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