Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting

Evolving neurocontrollers for the control of information diffusion in social networks

This paper presents a comparison of two Evolutionary Artificial Neural Network (EANN) variants acting as the autonomous control system for instances of the-Consensus Avoidance Problem ( θ-CAP). A novel variant of EANN is proposed by adopting characteristics of a well-performing heuristic into the structural bias of the neurocontroller. Information theoretic landscape measures are used to analyze the problem space as well as variants of the EANN. The results obtained indicate that the two neurocontroller variants learn distinctly different approaches to the θ-CAP, however, the newly proposed variant demonstrates improvements in both solution quality and execution time. A rampeddifficulty evolution scheme is demonstrated to be effective at creating higher quality results as compared to the standard scheme for EANNs. A correlation between the proposed instance difficulty and identifiable landscape characteristics is discovered as well

WASTE COLLECTION VEHICLE ROUTING PROBLEM WITH TIME WINDOWS USING MULTI-OBJECTIVE GENETIC ALGORITHMS

We study a waste collection vehicle routing problem with time windows (VRPTW) complicated by multiple disposal trips and driver’s lunch breaks. Recently Kim et al. [1] introduced and addressed this problem using an extension of the well-known Solomon’s insertion approach, and a clustering-based algorithm. We propose and present the results of an initial study of a multi-objective genetic algorithm for the waste collection VRPTW using a set of benchmark data from real-world problems obtained by Kim et al. KEY WORDS waste collection, vehicle routing problem with time windows, genetic algorithms, multi-objective optimization, Pareto ranking