Ubiquitous Computing and Distributed Agent-based Simulation

Abstract-As much as ubiquitous computing systems are already claimed to exist in the real world, further development of these systems still pose challenges to computer science that are still quite beyond the state of the art. Two challenges stand out in particular: the complexity of next-generation ubiquitous computing systems, and their inherent scalability issues. This paper aims to establish that agent-based modelling provides a powerful tool in tackling these issues. As an example of a practical solution, readily available, this paper highlights the distributed agent-based simulation infrastructure PDES-MAS as particularly suited for the task. Using the PDES-MAS infrastructure, designers, developers, and builders of next-generation ubiquitous computing systems can, through an iterative agent-based simulation process, gain the required knowledge and information about these systems, without having precede to deployment of the system itself

Hybrid Evolutionary Algorithms for Constraint Satisfaction Problems:

Abstract- We study a selected group of hybrid EAs for solving CSPs, consisting of the best performing EAs from the literature. We investigate the contribution of the evolutionary component to their performance by comparing the hybrid EAs with their “de-evolutionarised ” variants. The experiments show that “de-evolutionarising ” can increase performance, in some cases doubling it. Considering that the problem domain and the algorithms are arbitrarily selected from the “memetic niche”, it seems likely that the same effect occurs for other problems and algorithms. Therefore, our conclusion is that after designing and building a memetic algorithm, one should perform a verification by comparing this algorithm with its “de-evolutionarised” variant.

Sciences

Abstract- Evolutionary algorithms (EAs) for solving constraint satisfaction problems (CSPs) can be roughly divided into two classes: EAs using adaptive fitness functions and EAs using heuristics. In [8] the most effective EAs of the first class have been compared experimentally using a large set of benchmark instances consisting of randomly generated binary CSPs. In this paper we complete this comparison by studying the most effective EAs of the second class. We test three heuristic based EAs on the same benchmark instances used in [8]. The results of our experiments indicate that the three heuristic based EAs have similar performance on random binary CSPs. Moreover, comparing these results with those in [8], we are able to identify the best EA for binary CSPs as the algorithm introduced in [3] which uses a heuristic as well as an adaptive fitness function.

Comparing evolutionary algorithms on binary constraint satisfaction problems

Abstract — Constraint handling is not straightforward in evolutionary algorithms (ea) since the usual search operators, mutation and recombination, are ‘blind ’ to constraints. Nevertheless, the issue is highly relevant, for many challenging problems involve constraints. Over the last decade numerous eas for solving constraint satisfaction problems (csp) have been introduced and studied on various problems. The diversity of approaches and the variety of problems used to study the resulting algorithms prevents a fair and accurate comparison of these algorithms. This paper aligns related work by presenting a concise overview and an extensive performance comparison of all these eas on a systematically generated test suite of random binary csps. The random problem instance generator is based on a theoretical model that fixes deficiencies of models and respective generators that have been formerly used in the Evolutionary Computing (ec) field. Keywords—Constraint satisfaction problems, evolutionary algorithms, heuristics, adaptivity, problem instance generato