Superior Exploration-Exploitation Balance with Quantum-Inspired Hadamard Walks

This paper extends the analogies employed in the development of quantum-inspired evolutionary algorithms by proposing quantum-inspired Hadamard walks, called QHW. A novel quantum-inspired evolutionary algorithm, called HQEA, for solving combinatorial optimization problems, is also proposed. The novelty of HQEA lies in it's incorporation of QHW Remote Search and QHW Local Search - the quantum equivalents of classical mutation and local search, that this paper defines. The intuitive reasoning behind this approach, and the exploration-exploitation balance thus occurring is explained. From the results of the experiments carried out on the 0,1-knapsack problem, HQEA performs significantly better than a conventional genetic algorithm, CGA, and two quantum-inspired evolutionary algorithms - QEA and NQEA, in terms of convergence speed and accuracy.Comment: 2 pages, 2 figures, 1 table, late-breakin

Meta-Search Through the Space of Representations and Heuristics on a Problem by Problem Basis

Two key aspects of problem solving are representation and search heuristics. Both theoretical and experimental studies have shown that there is no one best problem representation nor one best search heuristic. Therefore, some recent methods, e.g., portfolios, learn a good combination of problem solvers to be used in a given domain or set of domains. There are even dynamic portfolios that select a particular combination of problem solvers specific to a problem. These approaches: (1) need to perform a learning step; (2) do not usually focus on changing the representation of the input domain/problem; and (3) frequently do not adapt the portfolio to the specific problem. This paper describes a meta-reasoning system that searches through the space of combinations of representations and heuristics to find one suitable for optimally solving the specific problem. We show that this approach can be better than selecting a combination to use for all problems within a domain and is competitive with state of the art optimal planners

Looking at gender disparity in science and mathematics from other angles: Are there differences in logical reasoning and linguistic abilities?

This study was carried out to further search for the true position of boys and girls in terms of participation and performance in science in classroom settings that are different from the conventional or traditional arrangement. The paper examined the differences in the logical reasoning, linguistic, reading as well as word-problem solving abilities of male and female science students which are considered important to effective learning of science. Forty science students consisting of 20 each of male and female groups were involved in the study. Each group was taught by four science teachers of the same sex for a period of six weeks. The teachers had uniform qualifications. In addition to the subject matter taught processes of science were emphasized throughout the lessons. The study showed that girls have higher achievement scores than boys in logical reasoning, linguistic, reading and word-problem solving abilities. The differences in the scores were also found to be significant at 0.05 confidence level. It was therefore concluded that the issue of gender influence on students‘ performance in science is not straight jacketed. Boys perform better than girls only in conventional classroom arrangements and in the overall science tasks but not in some tasks that are also very crucial to the learning of science.Keywords: gender differences in science achievement, logical reasoning, linguistic ability, reading ability word-problem solving abilit