A Prototype NSS Based on Problem Structure and Suggestions Toward More Comprehensive Negotiation Support

1996 ◽  
pp. 111-132
Author(s):  
Timothy R. Hill ◽  
Beth H. Jones
Keyword(s):  
Negotiation Support ◽  
Problem Structure

The graph model for conflicts as a negotiation support system

2002 ◽  
Author(s):  
D.M. Kilgour ◽  
L. Fang ◽  
K.W. Hipel
Keyword(s):  
Support System ◽  
Graph Model ◽  
Negotiation Support

A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

2021 ◽  
Vol 1 (2) ◽  
pp. 1-23
Author(s):  
Arkadiy Dushatskiy ◽  
Tanja Alderliesten ◽  
Peter A. N. Bosman
Keyword(s):  
Boolean Functions ◽  
Surrogate Model ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Optimal Solution ◽  
Problem Structure ◽  
Novel Approach ◽  
Efficient Learning ◽  
Nk Landscapes

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.

Choice of operation in verbal arithmetic problems: the effects of number size, problem structure and context

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

scholarly journals LACK OF FISHERY SUCCESSORS: ANALYTICAL METHODS FOR IDENTIFYING THE PROBLEM STRUCTURE AND FOR EVALUATING COUNTERMEASURES

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

Remarks on Conditions for the Validity of Parametric Decomposition in Optimal Design

1990 ◽  
Author(s):  
J. R. Jagannatha Rao ◽  
Panos Y. Papalambros
Keyword(s):  
Optimal Design ◽  
Present Article ◽  
Original Problem ◽  
Necessary Conditions ◽  
Special Problem ◽  
Problem Structure ◽  
Globally Convergent ◽  
Parametric Decomposition ◽  
Practical Design ◽  
Decomposition Strategies

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.

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