Visualising Evolution History in Multi- and Many-Objective Optimisation

Evolutionary algorithms are widely used to solve optimisation problems. However, challenges of transparency arise in both visualising the processes of an optimiser operating through a problem and understanding the problem features produced from many-objective problems, where comprehending four or more spatial dimensions is difficult. This work considers the visualisation of a population as an optimisation process executes. We have adapted an existing visualisation technique to multi- and many-objective problem data, enabling a user to visualise the EA processes and identify specific problem characteristics and thus providing a greater understanding of the problem landscape. This is particularly valuable if the problem landscape is unknown, contains unknown features or is a many-objective problem. We have shown how using this framework is effective on a suite of multi- and many-objective benchmark test problems, optimising them with NSGA-II and NSGA-III

Visualising Mutually Non-dominating Solution Sets in Many-objective Optimisation

Copyright © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.As many-objective optimization algorithms mature, the problem owner is faced with visualizing and understanding a set of mutually nondominating solutions in a high dimensional space. We review existing methods and present new techniques to address this problem. We address a common problem with the well-known heatmap visualization, since the often arbitrary ordering of rows and columns renders the heatmap unclear, by using spectral seriation to rearrange the solutions and objectives and thus enhance the clarity of the heatmap. A multiobjective evolutionary optimizer is used to further enhance the simultaneous visualization of solutions in objective and parameter space. Two methods for visualizing multiobjective solutions in the plane are introduced. First, we use RadViz and exploit interpretations of barycentric coordinates for convex polygons and simplices to map a mutually nondominating set to the interior of a regular convex polygon in the plane, providing an intuitive representation of the solutions and objectives. Second, we introduce a new measure of the similarity of solutions—the dominance distance—which captures the order relations between solutions. This metric provides an embedding in Euclidean space, which is shown to yield coherent visualizations in two dimensions. The methods are illustrated on standard test problems and data from a benchmark many-objective problem

Visualising many-objective populations

Copyright © 2012 ACM14th International Conference on Genetic and Evolutionary Computation (GECCO 2012), Philadelphia, USA, 7-11 July 2012Optimisation problems often comprise a large set of objectives, and visualising the set of solutions to a problem can help with understanding them, assisting a decision maker. If the set of objectives is larger than three, visualising solutions to the problem is a difficult task. Techniques for visualising high-dimensional data are often difficult to interpret. Conversely, discarding objectives so that the solutions can be visualised in two or three spatial dimensions results in a loss of potentially important information. We demonstrate four methods for visualising many-objective populations, two of which use the complete set of objectives to present solutions in a clear and intuitive fashion and two that compress the objectives of a population into two dimensions whilst minimising the information that is lost. All of the techniques are illustrated on populations of solutions to optimisation test problems

Virtual integration platform for computational fluid dynamics

Computational Fluid Dynamics (CFD) tools used in shipbuilding industry involve multiple disciplines, such as resistance, manoeuvring, and cavitation. Traditionally, the analysis was performed separately and sequentially in each discipline, which often resulted in conflict and inconsistency of hydrodynamic prediction. In an effort to solve such problems for future CFD computations, a Virtual Integration Platform (VIP) has been developed in the University of Strathclyde within two EU FP6 projects - VIRTUE and SAFEDOR1. The VIP provides a holistic collaborative environment for designers with features such as Project/Process Management, Distributed Tools Integration, Global Optimisation, Version Management, and Knowledge Management. These features enhance collaboration among customers, ship design companies, shipyards, and consultancies not least because they bring together the best expertise and resources around the world. The platform has been tested in seven European ship design companies including consultancies. Its main functionalities along with advances are presented in this paper with two industrial applications

Enabling dominance resistance in visualisable distance-based many-objective problems

The codebase for this paper is available at https://github.com/fieldsend/gecco_2016_vizThe results when optimising most multi- and many-objective problems are difficult to visualise, often requiring sophisticated approaches for compressing information into planar or 3D representations, which can be difficult to decipher. Given this, distance-based test problems are attractive: they can be constructed such that the designs naturally lie on the plane, and the Pareto set elements easy to identify. As such, distance-based problems have gained in popularity as a way to visualise the distribution of designs maintained by different optimisers. Some taxing problem aspects (many-to-one mappings and multi-modality) have been embedded into planar distance-based test problems, although the full range of problem characteristics which exist in other test problem frameworks (deceptive fronts, degeneracy, etc.) have not. Here we present an augmentation to the distance-based test problem formulation which induces dominance resistance regions, which are otherwise missing from these test problems. We illustrate the performance of two popular optimisers on test problems generated from this framework, and highlight particular problems with evolutionary search that can manifest due to the problem characteristics.This work was supported financially by the Engineering and Physical Sciences Research Council grant EP/M017915 /1

Understanding Optimisation Processes with Biologically-Inspired Visualisations

Evolutionary algorithms (EAs) constitute a branch of artificial intelligence utilised to evolve solutions to solve optimisation problems abound in industry and research. EAs often generate many solutions and visualisation has been a primary strategy to display EA solutions, given that visualisation is a multi-domain well-evaluated medium to comprehend extensive data. The endeavour of visualising solutions is inherent with challenges resulting from high dimensional phenomenons and the large number of solutions to display. Recently, scholars have produced methods to mitigate some of these known issues when illustrating solutions. However, one key consideration is that displaying the final subset of solutions exclusively (rather than the whole population) discards most of the informativeness of the search, creating inadequate insight into the black-box EA. There is an unequivocal knowledge gap and requirement for methods which can visualise the whole population of solutions from an optimiser and subjugate the high-dimensional problems and scaling issues to create interpretability of the EA search process. Furthermore, a requirement for explainability in evolutionary computing has been demanded by the evolutionary computing community, which could take the form of visualisations, to support EA comprehension much like the support explainable artificial intelligence has brought to artificial intelligence. In this thesis, we report novel visualisation methods that can be used to visualise large and high-dimensional optimiser populations with the aim of creating greater interpretability during a search. We consider the nascent intersection of visualisation and explainability in evolutionary computing. The potential high informativeness of a visualisation method from an early chapter of this work forms an effective platform to develop an explainability visualisation method, namely the population dynamics plot, to attempt to inject explainability into the inner workings of the search process. We further support the visualisation of populations using machine learning to construct models which can capture the characteristics of an EA search and develop intelligent visualisations which use artificial intelligence to potentially enhance and support visualisation for a more informative search process. The methods developed in this thesis are evaluated both quantitatively and qualitatively. We use multi-feature benchmark problems to show the method’s ability to reveal specific problem characteristics such as disconnected fronts, local optima and bias, as well as potentially creating a better understanding of the problem landscape and optimiser search for evaluating and comparing algorithm performance (we show the visualisation method to be more insightful than conventional metrics like hypervolume alone). One of the most insightful methods developed in this thesis can produce a visualisation requiring less than 1% of the time and memory necessary to produce a visualisation of the same objective space solutions using existing methods. This allows for greater scalability and the use in short compile time applications such as online visualisations. Predicated by an existing visualisation method in this thesis, we then develop and apply an explainability method to a real-world problem and evaluate it to show the method to be highly effective at explaining the search via solutions in the objective spaces, solution lineage and solution variation operators to compactly comprehend, evaluate and communicate the search of an optimiser, although we note the explainability properties are only evaluated against the author’s ability and could be evaluated further in future work with a usability study. The work is then supported by the development of intelligent visualisation models that may allow one to predict solutions in optima (importantly local optima) in unseen problems by using a machine learning model. The results are effective, with some models able to predict and visualise solution optima with a balanced F1 accuracy metric of 96%. The results of this thesis provide a suite of visualisations which aims to provide greater informativeness of the search and scalability than previously existing literature. The work develops one of the first explainability methods aiming to create greater insight into the search space, solution lineage and reproductive operators. The work applies machine learning to potentially enhance EA understanding via visualisation. These models could also be used for a number of applications outside visualisation. Ultimately, the work provides novel methods for all EA stakeholders which aims to support understanding, evaluation and communication of EA processes with visualisation

Identifying Good Algorithm Parameters in Evolutionary Multi- and Many-Objective Optimisation: A Visualisation Approach

Evolutionary algorithms are often highly dependent on the correct setting of their parameters, and benchmarking different parametrisations allows a user to identify which parameters offer the best performance on their given problem. Visualisation offers a way of presenting the results of such benchmarking so that a non-expert user can understand how their algorithm is performing. By examining the characteristics of their algorithm, such as convergence and diversity, the user can learn how effective their chosen algorithm parametrisation is. This paper presents a technique intended to offer this insight, by presenting the relative performance of a set of EAs optimising the same multi-objective problem in a simple visualisation. The visualisation characterises the behaviour of the algorithm in terms of known performance indicators drawn from the literature, and is capable of visualising the optimisation of many-objective problems also. The method is demonstrated with benchmark test problems from the popular DTLZ and CEC 2009 problem suites, optimising them with different parametrisations of both NSGA-II and NSGA-III, and it is shown that known characteristics of optimisers solving these problems can be observed in the visualisations resulting