SEARCH ENGINES USING EVOLUTIONARY ALGORITHMS

A subset of AI is, evolutionary algorithm (EA) which involves evolutionary computation, a generic populationbased meta heuristic optimization algorithm. An EA uses some mechanisms inspired by biological evolution: reproduction, mutation, recombination, and selection. A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Working of a search engine deals with searching for the indexed pages and referring to the related pages within a very short span of. Search engines commonly work through indexing. The paper deals with how a search engine works and how evolutionary algorithms can be used to develop a search engine that feeds on previous user requests to retrieve alternative documents that may not be returned by more conventional search engines

Heuristic usability evaluation on games: a modular approach

Heuristic evaluation is the preferred method to assess usability in games when experts conduct this evaluation. Many heuristics guidelines have been proposed attending to specificities of games but they only focus on specific subsets of games or platforms. In fact, to date the most used guideline to evaluate games usability is still Nielsen’s proposal, which is focused on generic software. As a result, most evaluations do not cover important aspects in games such as mobility, multiplayer interactions, enjoyability and playability, etc. To promote the usage of new heuristics adapted to different game and platform aspects we propose a modular approach based on the classification of existing game heuristics using metadata and a tool, MUSE (Meta-heUristics uSability Evaluation tool) for games, which allows a rebuild of heuristic guidelines based on metadata selection in order to obtain a customized list for every real evaluation case. The usage of these new rebuilt heuristic guidelines allows an explicit attendance to a wide range of usability aspects in games and a better detection of usability issues. We preliminarily evaluate MUSE with an analysis of two different games, using both the Nielsen’s heuristics and the customized heuristic lists generated by our tool.Unión Europea PI055-15/E0

TS2PACK: A Two-Level Tabu Search for the Three-dimensional Bin Packing Problem

Three-dimensional orthogonal bin packing is a problem NP-hard in the strong sense where a set of boxes must be orthogonally packed into the minimum number of three-dimensional bins. We present a two-level tabu search for this problem. The first-level aims to reduce the number of bins. The second optimizes the packing of the bins. This latter procedure is based on the Interval Graph representation of the packing, proposed by Fekete and Schepers, which reduces the size of the search space. We also introduce a general method to increase the size of the associated neighborhoods, and thus the quality of the search, without increasing the overall complexity of the algorithm. Extensive computational results on benchmark problem instances show the effectiveness of the proposed approach, obtaining better results compared to the existing one

Learning to solve planning problems efficiently by means of genetic programming

Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EVOCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator - Instance-Based Crossover - that is able to use traces of the base planner as raw genetic material to be injected into the evolving population.Publicad