Solution Attractor of Local Search System: A Method to Reduce Computational Complexity of the Traveling Salesman Problem

The traveling salesman problem (TSP) is presumably difficult to solve exactly using local search algorithms. It can be exactly solved by only one algorithm—the enumerative search algorithm. However, the scanning of all possible solutions requires exponential computing time. Do we need exploring all the possibilities to find the optimal solution? How can we narrow down the search space effectively and efficiently for an exhausted search? This chapter attempts to answer these questions. A local search algorithm is a discrete dynamical system, in which a search trajectory searches a part of the solution space and stops at a locally optimal point. A solution attractor of a local search system for the TSP is defined as a subset of the solution space that contains all locally optimal tours. The solution attractor concept gives us great insight into the computational complexity of the TSP. If we know where the solution attractor is located in the solution space, we simply completely search the solution attractor, rather than the entire solution space, to find the globally optimal tour. This chapter describes the solution attractor of local search system for the TSP and then presents a novel search system—the attractor-based search system—that can solve the TSP much efficiently with global optimality guarantee

Assessing the Knowledge Structure of Information Systems Learners in Experience-Based Learning

The fundamental goal of this study was to investigate the effects of an experience-based learning environment on information systems students\u27 knowledge structure. The learning environment was structured in a way consistent with the problem solving approaches used by information systems experts. The focus of this paper is to report the assessment of the knowledge structure of the information systems learners in a self-managed and experience-based learning environment. The key issue here is whether information systems students can develop the necessary cognitive skills in such learning environment. To assess the knowledge structure of the learners, this study designed three research instruments that included a declarative-knowledge test, a problem-solving task, and a similarity-judgment task. The analysis results suggested that the learning outcome in this experience-based learning environment was very positive. The environment that imposed an expert-like organization both on information gathering and on problem solving activities resulted in improved problem-solving skills. The learners mastered the necessary declarative knowledge, as well as developed domain-specific basic skill and strategies