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

Solution of intermediate-size problem and discrimination transfer by rats.

1969 ◽  
Vol 68 (2, Pt.1) ◽  
pp. 262-267 ◽  
Author(s):  
J. Jeffrey Laverty ◽  
Jack Werboff ◽  
Roger B. Frey
Keyword(s):  
Size Problem ◽  
Intermediate Size

Fleet size problem for Secure Food Chain with a certain range of transportation temperature

2020 ◽  
Author(s):  
Andrii Galkin ◽  
Tkachenko Iryna ◽  
Vladyka Yulia ◽  
Natalya Shyllye ◽  
Oksana Hulchak ◽  
...  
Keyword(s):  
Food Chain ◽  
Fleet Size ◽  
Size Problem

AnO(T2) Algorithm for theNI/G/NI/NDCapacitated Lot Size Problem

1988 ◽  
Vol 34 (3) ◽  
pp. 420-426 ◽  
Author(s):  
Chia-Shin Chung ◽  
Chien-Hua Mike Lin
Keyword(s):  
Lot Size ◽  
Size Problem

Discrete forecast horizons for two-product variants of the dynamic lot-size problem

2009 ◽  
Vol 120 (2) ◽  
pp. 430-436 ◽  
Author(s):  
Milind Dawande ◽  
Srinagesh Gavirneni ◽  
Sanjeewa Naranpanawe ◽  
Suresh P. Sethi
Keyword(s):  
Lot Size ◽  
Size Problem ◽  
Dynamic Lot Size

Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification

2007 ◽  
Vol 18 (2) ◽  
pp. 506-519 ◽  
Author(s):  
Marios Kyperountas ◽  
Anastasios Tefas ◽  
Ioannis Pitas
Keyword(s):  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Face Verification ◽  
Small Sample Size Problem ◽  
Size Problem

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.

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.

scholarly journals Bringing Kano’s Perspective to AHP: The 2D-AHP Decision Model

2016 ◽  
Vol 7 (4) ◽  
pp. 16-26
Author(s):  
Uk Jung ◽  
Seongmin Yim ◽  
Sunguk Lim ◽  
Chongman Kim
Keyword(s):  
Hybrid Model ◽  
Decision Model ◽  
Hybrid Approach ◽  
Survey Study ◽  
Test Case ◽  
Problem Structure ◽  
Kano Model ◽  
Two Dimension ◽  
True Hybrid ◽  
Decision Making Model

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.

Sign in / Sign up

Export Citation Format

Share Document