Distributed classifier migration in XCS for classification of electroencephalographic signals

This paper presents an investigation into combining migration strategies inspired by multi-deme Parallel Genetic Algorithms with the XCS Learning Classifier System to provide parallel and distributed classifier migration. Migrations occur between distributed XCS classifier sub-populations using classifiers ranked according to numerosity, fitness or randomly selected. The influence of the degree-of-connectivity introduced by Fully-Connected, Bi-directional Ring and Uni-directional Ring topologies is examined. Results indicate that classifier migration is an effective method for improving classification accuracy, improving learning speed and reducing final classifier population size, in the single-step classification of noisy, artefact-inclusive human electroencephalographic signals. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. © 2007 IEEE

A parallel and distributed genetic-based learning classifier system with application in human electroencephalographic signal classification

University of Technology, Sydney. Faculty of Engineering.Genetic-based Learning Classifier Systems have been proposed as a competent technology for the classification of medical data sets. What is not known about this class of system is twofold. Firstly, how does a Learning Classifier System (LCS) perform when applied to the single-step classification of multiple-channel, noisy, artefact-inclusive human EEG signals acquired from many participants? Secondly and more importantly, is how the learning classifier system performs when incorporated with migration strategies, inspired by multi- deme, coarse-grained Parallel Genetic Algorithms (PGA) to provide parallel and distributed classifier migration? This research investigates these open questions and concludes, subject to the considerations herein, that these technological approaches can provide competitive classification performance for such applications. We performed a preliminary examination and implementation of a parallel genetic algorithm and hybrid local search PGA using experimental methods. The parallelisation and incorporation of classical local search methods into a genetic algorithm are well known methods for increasing performance and we examine this. Furthermore, inspired by the significant improvements in convergence velocity and solution quality provided by the multi- deme, coarse-grained Parallel Genetic Algorithm, we incorporate the method into a learning classifier system with the aim of providing parallel and distributed classifier migration. As a result, a unique learning classifier system (pXCS) is proposed that improves classification accuracy, achieves increased learning rates and significantly reduces the classifier population during learning. It is compared to the extended learning Classifier System (XCS) and several state of the art non-evolutionary classifiers in the single-step classification of noisy, artefact- inclusive human EEG signals, derived from mental task experiments conducted using ten human participants. We also conclude that establishing an appropriate migration strategy is an important cause of pXCS learning and classification performance. However, an inappropriate migration rate, frequency or selection:replacement scheme can reduce performance and we document the factors associated with this. Furthermore, we conclude that both EEG segment size and representation both have a significant influence on classification performance. In effect, determining an appropriate representation of the raw EEG signal is tantamount to the classification method itself. This research allows us to further explore and incorporate pXCS evolved classifiers derived from multi-channel human EEG signals as an interface in the control of a device such as a powered wheelchair or brain-computer interface (BCI) applications

Parallelisation of genetic algorithms for the 2-page crossing number problem

Genetic algorithms have been applied to solve the 2-page crossing number problem successfully, but since they work with one global population, the search time and space are limited. Parallelisation provides an attractive prospect to improve the efficiency and solution quality of genetic algorithms. This paper investigates the complexity of parallel genetic algorithms (PGAs) based on two evaluation measures: Computation-time to Communication-time and Population-size to Chromosomesize. Moreover, the paper unifies the framework of PGA models with the function PGA (subpopulation size; cluster size, migration period; topology), and explores the performance of PGAs for the 2-page crossing number problem

Optimal distribution network reconfiguration using meta-heuristic algorithms

Finding optimal configuration of power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when time varying nature of loads in large-scale distribution systems is taken into account. In the second chapter of this dissertation, a systematic approach is proposed to tackle the computational burden of the procedure. To solve the optimization problem, a novel adaptive fuzzy based parallel genetic algorithm (GA) is proposed that employs the concept of parallel computing in identifying the optimal configuration of the network. The integration of fuzzy logic into GA enhances the efficiency of the parallel GA by adaptively modifying the migration rates between different processors during the optimization process. A computationally efficient graph encoding method based on Dandelion coding strategy is developed which automatically generates radial topologies and prevents the construction of infeasible radial networks during the optimization process. The main shortcoming of the proposed algorithm in Chapter 2 is that it identifies only one single solution. It means that the system operator will not have any option but relying on the found solution. That is why a novel hybrid optimization algorithm is proposed in the third chapter of this dissertation that determines Pareto frontiers, as candidate solutions, for multi-objective distribution network reconfiguration problem. Implementing this model, the system operator will have more flexibility in choosing the best configuration among the alternative solutions. The proposed hybrid optimization algorithm combines the concept of fuzzy Pareto dominance (FPD) with shuffled frog leaping algorithm (SFLA) to recognize non-dominated suboptimal solutions identified by SFLA. The local search step of SFLA is also customized for power systems applications so that it automatically creates and analyzes only the feasible and radial configurations in its optimization procedure which significantly increases the convergence speed of the algorithm. In the fourth chapter, the problem of optimal network reconfiguration is solved for the case in which the system operator is going to employ an optimization algorithm that is automatically modifying its parameters during the optimization process. Defining three fuzzy functions, the probability of crossover and mutation will be adaptively tuned as the algorithm proceeds and the premature convergence will be avoided while the convergence speed of identifying the optimal configuration will not decrease. This modified genetic algorithm is considered a step towards making the parallel GA, presented in the second chapter of this dissertation, more robust in avoiding from getting stuck in local optimums. In the fifth chapter, the concentration will be on finding a potential smart grid solution to more high-quality suboptimal configurations of distribution networks. This chapter is considered an improvement for the third chapter of this dissertation for two reasons: (1) A fuzzy logic is used in the partitioning step of SFLA to improve the proposed optimization algorithm and to yield more accurate classification of frogs. (2) The problem of system reconfiguration is solved considering the presence of distributed generation (DG) units in the network. In order to study the new paradigm of integrating smart grids into power systems, it will be analyzed how the quality of suboptimal solutions can be affected when DG units are continuously added to the distribution network. The heuristic optimization algorithm which is proposed in Chapter 3 and is improved in Chapter 5 is implemented on a smaller case study in Chapter 6 to demonstrate that the identified solution through the optimization process is the same with the optimal solution found by an exhaustive search

Sensitivity Analysis of Checkpointing Strategies for Multimemetic Algorithms on Unstable Complex Networks

The use of volatile decentralized computational platforms such as, e.g., peer-to-peer networks, is becoming an increasingly popular option to gain access to vast computing resources. Making an effective use of these resources requires algorithms adapted to such a changing environment, being resilient to resource volatility. We consider the use of a variant of evolutionary algorithms endowed with a classical fault-tolerance technique, namely the creation of checkpoints in a safe external storage. We analyze the sensitivity of this approach on different kind of networks (scale-free and small-world) and under different volatility scenarios. We observe that while this strategy is robust under low volatility conditions, in cases of severe volatility performance degrades sharply unless a high checkpoint frequency is used. This suggest that other fault-tolerance strategies are required in these situations.Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech. This work is partially supported by the MINECO project EphemeCH (TIN2014-56494-C4-1-P), by the Junta de Andalucía project DNEMESIS (P10-TIC-6083

Spacecraft Trajectory Optimization Suite (STOpS): Optimization of Multiple Gravity Assist Spacecraft Trajectories Using Modern Optimization Techniques

In trajectory optimization, a common objective is to minimize propellant mass via multiple gravity assist maneuvers (MGAs). Some computer programs have been developed to analyze MGA trajectories. One of these programs, Parallel Global Multiobjective Optimization (PaGMO), uses an interesting technique known as the Island Model Paradigm. This work provides the community with a MATLAB optimizer, STOpS, that utilizes this same Island Model Paradigm with five different optimization algorithms. STOpS allows optimization of a weighted combination of many parameters. This work contains a study on optimization algorithm performance and how each algorithm is affected by its available settings. STOpS successfully found optimal trajectories for the Mariner 10 mission and the Voyager 2 mission that were similar to the actual missions flown. STOpS did not necessarily find better trajectories than those actually flown, but instead demonstrated the capability to quickly and successfully analyze/plan trajectories. The analysis for each of these missions took 2-3 days each. The final program is a robust tool that has taken existing techniques and applied them to the specific problem of trajectory optimization, so it can repeatedly and reliably solve these types of problems