On the evolutionary optimisation of many conflicting objectives

This inquiry explores the effectiveness of a class of modern evolutionary algorithms, represented by Non-dominated Sorting Genetic Algorithm (NSGA) components, for solving optimisation tasks with many conflicting objectives. Optimiser behaviour is assessed for a grid of mutation and recombination operator configurations. Performance maps are obtained for the dual aims of proximity to, and distribution across, the optimal trade-off surface. Performance sweet-spots for both variation operators are observed to contract as the number of objectives is increased. Classical settings for recombination are shown to be suitable for small numbers of objectives but correspond to very poor performance for higher numbers of objectives, even when large population sizes are used. Explanations for this behaviour are offered via the concepts of dominance resistance and active diversity promotion

Generalized decomposition and cross entropy methods for many-objective optimization

Decomposition-based algorithms for multi-objective optimization problems have increased in popularity in the past decade. Although their convergence to the Pareto optimal front (PF) is in several instances superior to that of Pareto-based algorithms, the problem of selecting a way to distribute or guide these solutions in a high-dimensional space has not been explored. In this work, we introduce a novel concept which we call generalized decomposition. Generalized decomposition provides a framework with which the decision maker (DM) can guide the underlying evolutionary algorithm toward specific regions of interest or the entire Pareto front with the desired distribution of Pareto optimal solutions. Additionally, it is shown that generalized decomposition simplifies many-objective problems by unifying the three performance objectives of multi-objective evolutionary algorithms – convergence to the PF, evenly distributed Pareto optimal solutions and coverage of the entire front – to only one, that of convergence. A framework, established on generalized decomposition, and an estimation of distribution algorithm (EDA) based on low-order statistics, namely the cross-entropy method (CE), is created to illustrate the benefits of the proposed concept for many objective problems. This choice of EDA also enables the test of the hypothesis that low-order statistics based EDAs can have comparable performance to more elaborate EDAs

Elitism, Sharing and Ranking Choices in Evolutionary Multi-Criterion Optimisation

Elitism and sharing are two mechanisms that are believed to improve the performance of an evolutionary multi-criterion optimiser. The relative performance of of the two most popular ranking strategies is largely unknown. Using a new empirical inquiry framework, this report studies the effect of elitism, sharing and ranking design choices using a benchmark suite of two-criterion problems........

An Exploration of the Many-Objective Optimisation Process for a Class of Evolutionary Algorithms.

This empirical inquiry explores the behaviour of a particular class of evolutionary algorithms as the number of conflicting objectives to be simultaneously optimised is increased. Population-based optimisers that perform selection according to Pareto dominance and density estimation are considered. The performances of abstracted algorithms, based on decompositions of the fundamental components of a modern optimiser, are considered across a wide range of mutation and recombination operating conditions. Configuration sweet-spots for these algorithms are identified and contrasted. The classical mutation settings are shown to be a robust choice, even when the total sweet-spot is seen to contract as the number of objectives is increased. Classical settings for recombination, by contrast, are shown to work well for small numbers of objectives but lead to very poor performance as the number of objectives is increased. Mutation performance is demonstrated to be largely invariant of population size across the standard range of values. The performance of recombination can be somewhat improved by using larger population sizes. Explanations are offered for the observed behaviour of the evolutionary optimisers

The Multi-Objective Genetic Algorithm Applied to Benchmark Problems An Analysis

The multiobjective genetic algorithm (MOGA) has been applied to various real-world problems in a variety of fields, most prominently in control systems engineering, with considerable success. However, a recent empirical analysis of multi-objective evolutionary algorithms (MOEA's) has suggested that a MOGA-based algorithm performed poorly across a diverse set of two-objective test problems. In this report, it is shown that a conventional MOGA with standard settings can provide improved performance, but this still compares unfavourably to the best-performing contemporary MOEA, the Strength Pareto Evolutionary Algorithm (SPEA). The importance of the MOEA, as a framework is stressed and consequently, a real-coded MOGA for real-parameter multi-criterion problems is developed using modern gudelines for the design of evolutionary algorithms. This MOGA is shown to outperform the "best" MOEA, rather that a considered implementation of the methodology is required in order to reap full rewards. This study also questions the effectiveness of the traditional fitness sharing method of niching, with respect to the current set of multiobjective benchmark problems

Genetic Algorithms in Control Systems Engineering

Genetic algorithms (GA'S) are global, parallel, stochastic search methods, founded on Darwinian evolutionary principles. Many variations exist, including genetic programming and multi-objective algorithms. During the last decade GA's have been applied in a variety of areas, with varying degrees of success within each. A significant contribution has been made within control systems engineering. GA's exhibit considerable robustness in problem domains that are not conducive to formal, rigorous, classical analysis. They are not limited by typical control problem attributes such as ill- behaved objective functions, the existence of constraints and variations in the nature of control variables. GA software tools are available,but there is no "industry standard". The computational complexity of the GA has proved to be the chief impediment to real-time application of the technique . Hence, the majority of applications which use GA's are, by nature, off-line. GA's have been used to optimise both structure and parameter values for both controllers and plant models. They have also been applied to fault diagnosis, stability and analysis, robot path-planning and combinatorial problems (such as scheduling and bin-packing) Hybrid approaches have proved popular with GA's being integrated in fuzzy logic and neural computing schemes. The GA has been used as the population-based engine for multi-objective optimisers. Multiple, Pareto-optimal, solutions can be represented simultaneously. In such schemes, a decision maker can lead the direction of future search. Interesting future developments are anticipated in on-line applications and multi-objective search and decision making

Towards understanding the cost of adaptation in decomposition-based optimization algorithms

Decomposition-based methods are an increasingly popular choice for a posteriori multi-objective optimization. However the ability of such methods to describe a trade-off surface depends on the choice of weighting vectors defining the set of subproblems to be solved. Recent adaptive approaches have sought to progressively modify the weighting vectors to obtain a desirable distribution of solutions. This paper argues that adaptation imposes a non-negligible cost - in terms of convergence - on decomposition-based algorithms. To test this hypothesis, the process of adaptation is abstracted and then subjected to experimentation on established problems involving between three and 11 conflicting objectives. The results show that adaptive approaches require longer traversals through objectivespace than fixed-weight approaches. Since fixed weights cannot, in general, be specified in advance, it is concluded that the new wave of decomposition-based methods offer no immediate panacea to the well-known conflict between convergence and distribution afflicting Pareto-based a posteriori methods

From the Trenches: Cross-Campus Digital History Collaboration

In September 2015, our team launched The First World War Letters of H.J.C. Peirs (www.jackpeirs.org), a digital history initiative built on collaboration between faculty, students, and library staff. The project is founded on amazing primary source material, but with limited financial support and little dedicated staff time. We leveraged the creativity and hard work of our team members to build a website that is maintained by students and enhanced whenever possible with features and commentary from faculty and staff. Members of #TeamPeirs discussed the evolution of the project, the nature of our collaboration, and the intersection of audiences we have discovered