An interactive multiobjective programming approach to combinatorial data analysis

Combinatorial optimization problems in the social and behavioral sciences are frequently associated with a variety of alternative objective criteria. Multiobjective programming is an operations research methodology that enables the quantitative analyst to investigate tradeoffs among relevant objective criteria. In this paper, we describe an interactive procedure for multiobjective asymmetric unidimensional seriation problems. This procedure uses a dynamic-programming algorithm to partially generate the efficient set of sequences for small to medium-sized problems, and a multioperation heuristic to estimate the efficient set for larger problems. The interactive multiobjective procedure is applied to an empirical data set from the psychometric literature. We conclude with a discussion of other potential areas of application in combinatorial data analysis.Stephanie Stahl is a freelance writer and editor. She can be reached via e-mail at s-stahl@worldnet.att.net.     

Multi-objective combinatorial optimization problems: A survey

In the last 20 years many multi-objective linear programming (MOLP) methods with continuous variables have been developed. However, in many real-world applications discrete variables must be introduced. It is well known that MOLP problems with discrete variables can have special difficulties and so cannot be solved by simply combining discrete programming methods and multi-objective programming methods. The present paper is intended to review the existing literature on multi-objective combinatorial optimization (MOCO) problems. Various classical combinatorial problems are examined in a multi-criteria framework. Some conclusions are drawn and directions for future research are suggested.     

MOSA method: a tool for solving multiobjective combinatorial optimization problems

The success of modern heuristics (Simulated Annealing (S.A.), Tabu Search, Genetic Algorithms, …) in solving classical combinatorial optimization problems has drawn the attention of the research community in multicriteria methods. In fact, for large‐scale problems, the simultaneous difficulties of 𝒩𝒫‐hard complexity and of multiobjective framework make most Multiobjective Combinatorial Optimization (MOCO) problems intractable for exact methods. This paper develops the so‐called MOSA (Multiobjective Simulated Annealing) method to approximate the set of efficient solutions of a MOCO problem. Different options for the implementation are illustrated and extensive experiments prove the efficiency of the approach. Its results are compared to exact methods on bi‐objective knapsack problems. Copyright © 1999 John Wiley & Sons, Ltd.     

Derived relational responding as generalized operant behavior

The major aim of the present study was to demonstrate that derived relational responding may be viewed as a form of generalized operant behavior. In Experiment 1, 4 subjects were divided into two conditions (2 in each condition). Using a two-comparison matching-to-sample procedure, all subjects were trained and tested for the formation of two combinatorially entailed relations. Subjects were trained and tested across multiple stimulus sets. Each set was composed of novel stimuli. Both Conditions 1 and 2 involved explicit performance-contingent feedback presented at the end of each block of test trials (i.e., delayed feedback). In Condition 1, feedback was accurate (consistent with the experimenter-designated relations) following exposure to the initial stimulus sets. When subjects' responding reached a predefined mastery criterion, the feedback then switched to inaccurate (not consistent with the experimenter-designated relations) until responding once again reached a predefined criterion. Condition 2 was similar to Condition 1, except that exposure to the initial stimulus sets was followed by inaccurate feedback and once the criterion was reached feedback switched to accurate. Once relational responding emerged and stabilized, response patterns on novel stimulus sets were controlled by the feedback delivered for previous stimulus sets. Experiment 2 replicated Experiment 1, except that during Conditions 3 and 4 four comparison stimuli were employed during training and testing. Experiment 3 was similar to Condition 1 of Experiment 1, except that after the mastery criterion was reached for class-consistent responding, feedback alternated from accurate to inaccurate across each successive stimulus set. Experiment 4 involved two types of feedback, one type following tests for mutual entailment and the other type following tests for combinatorial entailment. Results from this experiment demonstrated that mutual and combinatorial entailment may be controlled independently by accurate and inaccurate feedback. Overall, the data support the suggestion, made by relational frame theory, that derived relational responding is a form of generalized operant behavior.     

Matrix reorganization and dynamic programming: Applications to paired comparisons and unidimensional seriation

A recursive dynamic programming strategy is discussed for optimally reorganizing the rows and simultaneously the columns of ann ×n proximity matrix when the objective function measuring the adequacy of a reorganization has a fairly simple additive structure. A number of possible objective functions are mentioned along with several numerical examples using Thurstone's paired comparison data on the relative seriousness of crime. Finally, the optimization tasks we propose to attack with dynamic programming are placed in a broader theoretical context of what is typically referred to as the quadratic assignment problem and its extension to cubic assignment.Partial support for this research was provided by NIJ Grant 80-IJ-CX-0061.