Mixed-Initiative Systems (MIS) are hybrid decision systems where collaboration is possible between humans and machines. However, current systems sometimes override user preferences when provided with new ones. We studied linear optimization problems, where the decision maker is specifying preferences for variable values through an iterative process. We proposed a goal programming framework to d...

We consider voting over combinatorial domains, where alternatives are binary tuples. We assume that votes are specified as conditionally lexicographic preferences, or LP trees. We study aggregation problems of LP tree votes for several positional scoring rules. Our main goal is to demonstrate that answer-set programming can be effective in solving the winner and the evaluation problems for inst...

this paper investigates a procedure for identifying all efficient hyperplanes of production possibility set (pps). this procedure is based on a method which recommended by pekka j. korhonen[8]. he offered using of lexicographic parametric programming method for recognizing all efficient units in data envelopment analysis (dea). in this paper we can find efficient hyperplanes, via using the para...

We propose a new polarization-preserving edge-lit light guide plate (LGP) based on total internal reflection. Our simulation results show that such a LGP exhibits several attractive features: 1.54X higher polarization efficiency, 2.4X higher on-axis luminance, and no need for a brightness enhancement film. When integrated with a linearly polarized LED array, the LCD optical efficiency can be en...

A developmental co-evolutionary genetic programming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks in the technology sector. Both interday and intraday data for these stocks were analyzed, where both implementations were found to be impressively robust to market fluctuations while reacting efficiently to opportunities for profi...

Genetic programming (GP), a relatively young and growing branch of evolutionary computation is gradually proving to be a promising method of modelling complex prediction and classification problems. This paper evaluates the suitability of a linear genetic programming (LGP) technique to predict electricity demand in the State of Victoria, Australia, while comparing its performance with two other...

In this paper, a generational Evolutionary Algorithm (EA) for function optimization is evolved using the Linear Genetic Programming (LGP) technique. Numerical experiments show that the evolved EA significantly outperforms a standard GA.

This paper presents a fuzzy goal programming FGP procedure for solving bilevel multiobjective linear fractional programming BL-MOLFP problems. It makes an extension work of Moitra and Pal 2002 and Pal et al. 2003 . In the proposed procedure, the membership functions for the defined fuzzy goals of the decision makers DMs objective functions at both levels as well as the membership functions for ...