Elite Accumulative Sampling Strategies for Noisy Multi-Objective Optimisation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-15892-1_128th International Conference on Evolutionary Multi-Criterion Optimization 2015, Guimarães, Portugal, 29 March - 1 April 1 2015The codebase for this paper is available at https://github.com/fieldsend/EMO_2015_eliteWhen designing evolutionary algorithms one of the key concerns is the balance between expending function evaluations on exploration versus exploitation. When the optimisation problem experiences observational noise, there is also a trade-off with respect to accuracy refinement – as improving the estimate of a design’s performance typically is at the cost of additional function reevaluations. Empirically the most effective resampling approach developed so far is accumulative resampling of the elite set. In this approach elite members are regularly reevaluated, meaning they progressively accumulate reevaluations over time. This results in their approximated objective values having greater fidelity, meaning non-dominated solutions are more likely to be correctly identified. Here we examine four different approaches to accumulative resampling of elite members, embedded within a differential evolution algorithm. Comparing results on 40 variants of the unconstrained IEEE CEC’09 multi-objective test problems, we find that at low noise levels a low fixed resample rate is usually sufficient, however for larger noise magnitudes progressively raising the number of minimum resamples of elite members based on detecting estimated front oscillation tends to improve performance

On the Exploitation of Search History and Accumulative Sampling in Robust Optimisation

This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.Efficient robust optimisation methods exploit the search history when evaluating a new solution by using information from previously visited solutions that fall in the new solution’s uncertainty neighbourhood. We propose a full exploitation of the search history by updating the robust fitness approximations across the entire search history rather than a fixed population. Our proposed method shows promising results on a range of test problems compared with other approaches from the literature.This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N017846/1]

Efficiently identifying pareto solutions when objective values change

Copyright © 2014 ACMThe example code for this paper is available at https://github.com/fieldsend/gecco_2014_changing_objectivesIn many multi-objective problems the objective values assigned to a particular design can change during the course of an optimisation. This may be due to dynamic changes in the problem itself, or updates to estimated objectives in noisy problems. In these situations, designs which are non-dominated at one time step may become dominated later not just because a new and better solution has been found, but because the existing solution's performance has degraded. Likewise, a dominated solution may later be identified as non-dominated because its objectives have comparatively improved. We propose management algorithms based on recording single “guardian dominators" for each solution which allow rapid discovery and updating of the non-dominated subset of solutions evaluated by an optimiser. We examine the computational complexity of our proposed approach, and compare the performance of different ways of selecting the guardian dominators

Interval-based ranking in noisy evolutionary multiobjective optimization

As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many realworld multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling noisy objectives in evolutionary multi-objective optimization algorithms becomes very important and is gaining more attention in recent years. In this paper we present ?-degree Pareto dominance relation for ordering the solutions in multi-objective optimization when the values of the objective functions are given as intervals. Based on this dominance relation, we propose an adaptation of the non-dominated sorting algorithm for ranking the solutions. This ranking method is then used in a standardmulti-objective evolutionary algorithm and a recently proposed novel multi-objective estimation of distribution algorithm based on joint variable-objective probabilistic modeling, and applied to a set of multi-objective problems with different levels of independent noise. The experimental results show that the use of the proposed method for solution ranking allows to approximate Pareto sets which are considerably better than those obtained when using the dominance probability-based ranking method, which is one of the main methods for noise handling in multi-objective optimization

The Rolling Tide Evolutionary Algorithm: A Multi-Objective Optimiser for Noisy Optimisation Problems

As the methods for evolutionary multiobjective optimization (EMO) mature and are applied to a greater number of real-world problems, there has been gathering interest in the effect of uncertainty and noise on multiobjective optimization, specifically how algorithms are affected by it, how to mitigate its effects, and whether some optimizers are better suited to dealing with it than others. Here we address the problem of uncertain evaluation, in which the uncertainty can be modeled as an additive noise in objective space. We develop a novel algorithm, the rolling tide evolutionary algorithm (RTEA), which progressively improves the accuracy of its estimated Pareto set, while simultaneously driving the front toward the true Pareto front. It can cope with noise whose characteristics change as a function of location (both design and objective), or which alter during the course of an optimization. Four state-of-the-art noise-tolerant EMO algorithms, as well as four widely used standard EMO algorithms, are compared to RTEA on 70 instances of ten continuous space test problems from the CEC'09 multiobjective optimization test suite. Different instances of these problems are generated by modifying them to exhibit different types and intensities of noise. RTEA seems to provide competitive performance across both the range of test problems used and noise types

Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment

The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging computer vision tasks. Meanwhile, the development of neural architecture search (NAS) has provided promising approaches to automating the design of network architectures for lower prediction error. Recently, the emerging application scenarios of deep learning have raised higher demands for network architectures considering multiple design criteria: number of parameters/floating-point operations, and inference latency, among others. From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them. Nonetheless, there is still a clear gap confining the related research along this pathway: on the one hand, there is a lack of a general problem formulation of NAS tasks from an optimization point of view; on the other hand, there are challenges in conducting benchmark assessments of EMO algorithms on NAS tasks. To bridge the gap: (i) we formulate NAS tasks into general multi-objective optimization problems and analyze the complex characteristics from an optimization point of view; (ii) we present an end-to-end pipeline, dubbed $\texttt{EvoXBench}$, to generate benchmark test problems for EMO algorithms to run efficiently -- without the requirement of GPUs or Pytorch/Tensorflow; (iii) we instantiate two test suites comprehensively covering two datasets, seven search spaces, and three hardware devices, involving up to eight objectives. Based on the above, we validate the proposed test suites using six representative EMO algorithms and provide some empirical analyses. The code of $\texttt{EvoXBench}$ is available from $\href{https://github.com/EMI-Group/EvoXBench}{\rm{here}}$

Combining evolutionary algorithms and agent-based simulation for the development of urbanisation policies

Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. To help in these decision-making processes, this thesis provides an empirical study of using evolutionary approaches to solve sequential decision making problems under uncertainty in stochastic environments. To achieve this goal, this work is underpinned by developing a theoretical framework based on the economic model of Alonso and the associated methodology for modelling spatial and temporal urban growth, in order to better understand the complexity inherent in this kind of system and to generate and improve relevant knowledge for the urban planning community. The model was hybridised with cellular automata and agent-based model and extended to encompass green space planning based on urban cost and satisfaction. Monte Carlo sampling techniques and the use of the urban model as a surrogate tool were the two main elements investigated and applied to overcome the noise and uncertainty derived from dealing with future trends and expectations. Once the evolutionary algorithms were equipped with these mechanisms, the problem under consideration was defined and characterised as a type of adaptive submodular. Afterwards, the performance of a non-adaptive evolutionary approach with a random search and a very smart greedy algorithm was compared and in which way the complexity that is linked with the configuration of the problem modifies the performance of both algorithms was analysed. Later on, the application of very distinct frameworks incorporating evolutionary algorithm approaches for this problem was explored: (i) an ‘offline’ approach, in which a candidate solution encodes a complete set of decisions, which is then evaluated by full simulation, and (ii) an ‘online’ approach which involves a sequential series of optimizations, each making only a single decision, and starting its simulations from the endpoint of the previous run

Microbial community pattern detection in human body habitats via ensemble clustering framework

The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural patterns. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.Comment: BMC Systems Biology 201

On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments

Sampling has been often employed by evolutionary algorithms to cope with noise when solving noisy real-world optimization problems. It can improve the estimation accuracy by averaging over a number of samples, while also increasing the computation cost. Many studies focused on designing efficient sampling methods, and conflicting empirical results have been reported. In this paper, we investigate the effectiveness of sampling in terms of rigorous running time, and find that sampling can be ineffective. We provide a general sufficient condition under which sampling is useless (i.e., sampling increases the running time for finding an optimal solution), and apply it to analyzing the running time performance of (1+1)-EA for optimizing OneMax and Trap problems in the presence of additive Gaussian noise. Our theoretical analysis indicates that sampling in the above examples is not helpful, which is further confirmed by empirical simulation results