Evolutionary Computation in High Energy Physics

Evolutionary Computation is a branch of computer science with which, traditionally, High Energy Physics has fewer connections. Its methods were investigated in this field, mainly for data analysis tasks. These methods and studies are, however, less known in the high energy physics community and this motivated us to prepare this lecture. The lecture presents a general overview of the main types of algorithms based on Evolutionary Computation, as well as a review of their applications in High Energy Physics.Comment: Lecture presented at 2006 Inverted CERN School of Computing; to be published in the school proceedings (CERN Yellow Report

An artificial life approach to evolutionary computation: from mobile cellular algorithms to artificial ecosystems

This thesis presents a new class of evolutionary algorithms called mobile cellular evolutionary algorithms (mcEAs). These algorithms are characterized by individuals moving around on a spatial population structure. As a primary objective, this thesis aims to show that by controlling the population density and mobility in mcEAs, it is possible to achieve much better control over the rate of convergence than what is already possible in existing cellular EAs. Using the observations and results from this investigation into selection pressure in mcEAs, a general architecture for developing agent-based evolutionary algorithms called Artificial Ecosystems (AES) is presented. A simple agent-based EA is developed within the scope of AES is presented with two individual-based bottom-up schemes to achieve dynamic population sizing. Experiments with a test suite of optimization problems show that both mcEAs and the agent-based EA produced results comparable to the best solutions found by cellular EAs --Abstract, page iii

Cooperation of Nature and Physiologically Inspired Mechanism in Visualisation

A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised. The performance of the nature-inspired algorithms and the biologically inspired mechanisms in the hybrid algorithm is reflected through a cooperative attempt to make a drawing on the canvas. The scientific value of the marriage between the two swarm intelligence algorithms is currently being investigated thoroughly on many benchmarks and the results reported suggest a promising prospect (al-Rifaie, Bishop & Blackwell, 2011). We also discuss whether or not the ‘art works’ generated by nature and biologically inspired algorithms can possibly be considered as ‘computationally creative’

Performance evaluation of WMN-GA for different mutation and crossover rates considering number of covered users parameter

Node placement problems have been long investigated in the optimization field due to numerous applications in location science and classification. Facility location problems are showing their usefulness to communication networks, and more especially from Wireless Mesh Networks (WMNs) field. Recently, such problems are showing their usefulness to communication networks, where facilities could be servers or routers offering connectivity services to clients. In this paper, we deal with the effect of mutation and crossover operators in GA for node placement problem. We evaluate the performance of the proposed system using different selection operators and different distributions of router nodes considering number of covered users parameter. The simulation results show that for Linear and Exponential ranking methods, the system has a good performance for all rates of crossover and mutation.Peer ReviewedPostprint (published version

GLOBAL OPTIMIZATION METHODS

Training a neural network is a difficult optimization problem because of numerous local minimums. Many global search algorithms have been used to train neural networks. However, local search algorithms are more efficient with computational resources, and therefore numerous random restarts with a local algorithm may be more effective than a global algorithm. This study uses Monte-Carlo simulations to determine the relative efficiency of a local search algorithm to 9 stochastic global algorithms. The computational requirements of the global algorithms are several times higher than the local algorithm and there is little gain in using the global algorithms to train neural networks.Research Methods/ Statistical Methods,

A Survey of Parallel Data Mining

With the fast, continuous increase in the number and size of databases, parallel data mining is a natural and cost-effective approach to tackle the problem of scalability in data mining. Recently there has been a considerable research on parallel data mining. However, most projects focus on the parallelization of a single kind of data mining algorithm/paradigm. This paper surveys parallel data mining with a broader perspective. More precisely, we discuss the parallelization of data mining algorithms of four knowledge discovery paradigms, namely rule induction, instance-based learning, genetic algorithms and neural networks. Using the lessons learned from this discussion, we also derive a set of heuristic principles for designing efficient parallel data mining algorithms

Comparison of Selection Methods in On-line Distributed Evolutionary Robotics

In this paper, we study the impact of selection methods in the context of on-line on-board distributed evolutionary algorithms. We propose a variant of the mEDEA algorithm in which we add a selection operator, and we apply it in a taskdriven scenario. We evaluate four selection methods that induce different intensity of selection pressure in a multi-robot navigation with obstacle avoidance task and a collective foraging task. Experiments show that a small intensity of selection pressure is sufficient to rapidly obtain good performances on the tasks at hand. We introduce different measures to compare the selection methods, and show that the higher the selection pressure, the better the performances obtained, especially for the more challenging food foraging task