Investigating hybrids of evolution and learning for real-parameter optimization

In recent years, more and more advanced techniques have been developed in the field of hybridizing of evolution and learning, this means that more applications with these techniques can benefit from this progress. One example of these advanced techniques is the Learnable Evolution Model (LEM), which adopts learning as a guide for the general evolutionary search. Despite this trend and the progress in LEM, there are still many ideas and attempts which deserve further investigations and tests. For this purpose, this thesis has developed a number of new algorithms attempting to combine more learning algorithms with evolution in different ways. With these developments, we expect to understand the effects and relations between evolution and learning, and also achieve better performances in solving complex problems. The machine learning algorithms combined into the standard Genetic Algorithm (GA) are the supervised learning method k-nearest-neighbors (KNN), the Entropy-Based Discretization (ED) method, and the decision tree learning algorithm ID3. We test these algorithms on various real-parameter function optimization problems, especially the functions in the special session on CEC 2005 real-parameter function optimization. Additionally, a medical cancer chemotherapy treatment problem is solved in this thesis by some of our hybrid algorithms. The performances of these algorithms are compared with standard genetic algorithms and other well-known contemporary evolution and learning hybrid algorithms. Some of them are the CovarianceMatrix Adaptation Evolution Strategies (CMAES), and variants of the Estimation of Distribution Algorithms (EDA). Some important results have been derived from our experiments on these developed algorithms. Among them, we found that even some very simple learning methods hybridized properly with evolution procedure can provide significant performance improvement; and when more complex learning algorithms are incorporated with evolution, the resulting algorithms are very promising and compete very well against the state of the art hybrid algorithms both in well-defined real-parameter function optimization problems and a practical evaluation-expensive problem

Testing Vision-Based Control Systems Using Learnable Evolutionary Algorithms

Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorithms rely on machine learning or a combination of machine learning and Darwinian genetic operators to guide the generation of new solutions (test scenarios in our context). Our approach combines multiobjective population-based search algorithms and decision tree classification models to achieve the following goals: First, classification models guide the search-based generation of tests faster towards critical test scenarios (i.e., test scenarios leading to failures). Second, search algorithms refine classification models so that the models can accurately characterize critical regions (i.e., the regions of a test input space that are likely to contain most critical test scenarios). Our evaluation performed on an industrial automotive vision-based control system shows that: (1) Our algorithm outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm. (2) Our algorithm accurately characterizes critical regions of the system under test, thus identifying the conditions that likely to lead to system failures

Effective Testing Of Advanced Driver Assistance Systems Using Evolutionary Algorithms And Machine Learning

Improving road safety is a major concern for most car manufacturers. In recent years, the development of Advanced Driver Assistance Systems (ADAS) has subsequently seen a tremendous boost. The development of such systems requires complex testing to ensure vehicle’s safety and reliability. Performing road tests tends to be dangerous, time-consuming, and costly. Hence, a large part of testing for ADAS has to be carried out using physics-based simulation platforms, which are able to emulate a wide range of virtual traffic scenarios and road environments. The main difficulties with simulation-based testing of ADAS are: (1) the test input space is large and multidimensional, (2) simulation platforms provide no guidance to engineers as to which scenarios should be selected for testing, and hence, simulation is limited to a small number of scenarios hand-picked by engineers, and (3) test executions are computationally expensive because they often involve executing high-fidelity mathematical models capturing continuous dynamic behaviors of vehicles and their environment. The complexity of testing ADAS is further exacerbated when many ADAS are employed together in a self-driving system. In particular, when self-driving systems include many ADAS (i.e., features), they tend to interact and impact one another’s behavior in an unknown way and may lead to conflicting situations. The main challenge here is to detect and manage feature interactions, in particular, those that violate system safety requirements, hence leading to critical failures. In practice, once feature interaction failures are detected, engineers need to devise resolution strategies to resolve potential conflicts between features. Developing resolution strategies is a complex task and despite the extensive domain expertise, these resolution strategies can be erroneous and are too complex to be manually repaired. In this dissertation, in addition to testing individual ADAS, we focus on testing self-driving systems that include several ADAS. In this dissertation, we propose a set of approaches based on meta-heuristic search and machine learning techniques to automate ADAS testing and to repair feature interaction failures in self-driving systems. The work presented in this dissertation is motivated by ADAS testing needs at IEE, a world-leading part supplier to the automotive industry. In this dissertation, we focus on the problem of design time testing of ADAS in a simulated environment, relying on Simulink models. The main research contributions in this dissertation are: - A testing approach for ADAS that combines multi-objective search with surrogate models to guide testing towards the most critical behaviors of ADAS, and to explore a larger part of the input search space with less computational resources. - An automated testing algorithm that builds on learnable evolution models and uses classification decision trees to guide the generation of new test scenarios within complex and multidimensional input spaces and help engineers interpret test results. - An automated technique that detects feature interaction failures in the context of self-driving systems based on analyzing executable function models typically developed to specify system behaviors at early development stages. - An automated technique that uses a new many-objective search algorithm to localize and repair errors in the feature interaction resolution rules for self-driving systems

Artificial Intelligence Techniques for Flood Risk Management in Urban Environments

Flooding is an important concern for the UK, as evidenced by the many extreme flooding events in the last decade. Improved flood risk intervention strategies are therefore highly desirable. The application of hydroinformatics tools, and optimisation algorithms in particular, which could provide guidance towards improved intervention strategies, is hindered by the necessity of performing flood modelling in the process of evaluating solutions. Flood modelling is a computationally demanding task; reducing its impact upon the optimisation process would therefore be a significant achievement and of considerable benefit to this research area. In this thesis sophisticated multi-objective optimisation algorithms have been utilised in combination with cutting-edge flood-risk assessment models to identify least-cost and most-benefit flood risk interventions that can be made on a drainage network. Software analysis and optimisation has improved the flood risk model performance. Additionally, artificial neural networks used as feature detectors have been employed as part of a novel development of an optimisation algorithm. This has alleviated the computational time-demands caused by using extremely complex models. The results from testing indicate that the developed algorithm with feature detectors outperforms (given limited computational resources available) a base multi-objective genetic algorithm. It does so in terms of both dominated hypervolume and a modified convergence metric, at each iteration. This indicates both that a shorter run of the algorithm produces a more optimal result than a similar length run of a chosen base algorithm, and also that a full run to complete convergence takes fewer iterations (and therefore less time) with the new algorithm.EPSRCSTREAM-ID

The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems

ABSTRACT 1 Learnable Evolution Model (LEM) is a form of non-Darwinian evolutionary computation that employs machine learning to guide evolutionary processes. Its main novelty are new type of operators for creating new individuals, specifically, hypothesis generation, which learns rules indicating subareas in the search space that likely contain the optimum, and hypothesis instantiation, which populates these subspaces with new individuals. This paper briefly describes the newest and most advanced implementation of learnable evolution, LEM3, its novel features, and results from its comparison with a conventional, Darwinian-type evolutionary computation program (EA), a cultural evolution algorithm (CA), and the estimation of distribution algorithm (EDA) on selected function optimization problems (with the number of variables varying up to 1000). In every experiment, LEM3 outperformed the compared programs in terms of the evolution length (the number of fitness evaluations needed to achieved a desired solution), sometimes more than by one order of magnitude

The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems

This report presents a User’s Guide for the LEM3 program implementing Learnable Evolution Model, a machine learning-guided form of evolutionary computation. In contrast to the previous implementations, LEM3 employs the newest AQ21 machine learning program and includes several extensions and new features. The new features include a deep integration of machine learning and conventional evolution modes, capabilities for defining the fitness evaluation function outside of the main program, setting up LEM3 parameters, and deciding what information to be outputted by the program. LEM3 appears to be particularly suitable for solving complex optimization problems in which the fitness evaluation function is time-consuming, Another important feature is that it can be applied to the optimization of non-numerical problems involving multitype attributes