Fractional Stochastic Search Algorithms: Modelling Complex Systems via AI

Software Pipelining via Stochastic Search Algorithms

The scheduling of loops for architectures which support instruction level parallelism is an important area of research. Many polynomial time, heuristic algorithms for software pipelining have been proposed for this NP-complete problem. In this research, genetic algorithms and simulated annealing are used to test the feasibility of applying artiicial intelligence techniques to the problem of sof...

Can Empirical Modelling Facilitate Learning?: Modelling Graphs to Assist in the Teaching of AI Search Algorithms

This paper aims to critically assess the claim that Empirical Modelling (EM) tools and techniques can be used to facilitate learning. In Artificial Intelligence, algorithms such as A*, Greedy Best First Search and Dijkstra’s algorithm are taught in order to find the most efficient path from one node to another in a graph. When teaching the behaviour of these algorithms, it can often be difficul...

Improving Search-Based AI Systems

Stochastic Local Search Algorithms

The Genomic Median Problem is an optimization problem inspired by a biological issue: it aims at finding the genome organization of the common ancestor to multiple living species. It is formulated as the search for a genome that minimizes some distance measure among given genomes. Several attempts have been made at solving the problem. These range from simple heuristic methods to a stochastic l...

Stochastic Service Systems, Random Interval Graphs and Search Algorithms

We consider several stochastic service systems, and study the asymptotic behavior of the moments of various quantities that have application to models for random interval graphs and algorithms for searching for an idle server or empty waiting station. In two cases the moments turn out to involve Lambert series for the generating functions for the sums of powers of divisors of positive integers....