A “standard” map provides a simplified representation of the real world but is not able to adapt to the needs of different users having different preferences. On the contrary, adaptive maps are aimed at representing the world as seen through the eyes of the user depicting the suitability/difficulty of the paths between points of interest according to the user's preferences. Adaptive maps consider, simultaneously, multiple attributes like travel time, distance, slopes, etc. In this paper, a model for adaptive maps is presented. A generation and a visualization methods are proposed. To illustrate the results, five different adaptive maps are generated and visualized considering different attributes and preferences.

Given a multicriteria decision-making problem, an obvious question emerges: Which method should be used to solve it? Although some efforts had been made, the question remains open. The aim of this contribution is to compare a set of multicriteria decision-making methods sharing three features: same fuzzy information as input data, the need of a data normalization procedure, and quite similar information processing. We analyze the rankings produced by fuzzy MULTIMOORA, fuzzy TOPSIS (with two normalizations), fuzzy VIKOR, and fuzzy WASPAS with different parameterizations, over 1200 randomly generated decision problems. The results clearly show their similarities and differences, the impact of the parameters settings, and how the methods can be clustered, thus providing some guidelines for their selection and usage.

This work presents a driving system designed for virtual racing situations. It is based on a complete modular architecture capable of automatically driving a car along a track with or without opponents. The architecture is composed of intuitive modules, with each one being responsible for a basic aspect of car driving. Moreover, this modularity of the architecture will allow us to replace or add modules in the future as a way to enhance particular features of particular situations. In the present work, some of the modules are implemented by means of hand-designed driving heuristics, whereas modules responsible for adapting the speed and direction of the vehicle to the track's shape, both critical aspects of driving a vehicle, are optimized by means of a genetic algorithm that evaluates the performance of the controller in four different tracks to obtain the best controller in a large number of situations; the algorithm also penalizes controllers that go out of the track, lose control, or get damaged. The evaluation of the performance is done in two ways. First, in runs with and without adversaries over several tracks. And second, the architecture was submitted as a participant to the 2010 Simulated Car Racing Competition, which in end won laurels. © 2012 Wiley Periodicals, Inc.

In recent years, biological and natural processes have been increasingly influencing methodologies in science and technology. In particular, the role played by the cooperation among individuals is being studied more frequently and profoundly in diverse areas of knowledge. We present here a multiagent decentralized strategy for dynamic optimization problems where a population of cooperative agents and solutions are used to deal with the moving peaks problem. We focus on cooperation and diversity mechanisms, and we study how different alternatives affect the performance of the strategy. © 2009 Wiley Periodicals, Inc.

The interface between combinatorial optimization and fuzzy sets-based methodologies is the subject of very active and increasing research. In this context we describe FANS, a fuzzy adaptive neighborhood search optimization heuristic that uses a fuzzy valuation to qualify solutions and adapts its behavior as a function of the search state. FANS may also be regarded as a local search framework. We show the application of this fuzzy sets-based heuristic to the protein structure prediction problem in two aspects: first, to analyze how the codification of the solutions affects the results, and second, to confirm that FANS is able to obtain as good results as a genetic algorithm. Both results shed some light on the application of heuristics to the protein structure prediction problem and show the benefits and power of combining basic fuzzy sets ideas with heuristic techniques. © 2002 Wiley Periodicals, Inc.