Bayesian network structure learning using characteristic properties of permutation representations with applications to prostate cancer treatment.

Over the last decades, Bayesian Networks (BNs) have become an increasingly popular technique to model data under presence of uncertainty. BNs are probabilistic models that represent relationships between variables by means of a node structure and a set of parameters. Learning efficiently the structure that models a particular dataset is a NP-hard task that requires substantial computational efforts to be successful. Although there exist many families of techniques for this purpose, this thesis focuses on the study and improvement of search and score methods such as Evolutionary Algorithms (EAs). In the domain of BN structure learning, previous work has investigated the use of permutations to represent variable orderings within EAs. In this thesis, the characteristic properties of permutation representations are analysed and used in order to enhance BN structure learning. The thesis assesses well-established algorithms to provide a detailed analysis of the difficulty of learning BN structures using permutation representations. Using selected benchmarks, rugged and plateaued fitness landscapes are identified that result in a loss of population diversity throughout the search. The thesis proposes two approaches to handle the loss of diversity. First, the benefits of introducing the Island Model (IM) paradigm are studied, showing that diversity loss can be significantly reduced. Second, a novel agent-based metaheuristic is presented in which evolution is based on the use of several mutation operators and the definition of a distance metric in permutation spaces. The latter approach shows that diversity can be maintained throughout the search while exploring efficiently the solution space. In addition, the use of IM is investigated in the context of distributed data, a common property of real-world problems. Experiments prove that privacy can be preserved while learning BNs of high quality. Finally, using UK-wide data related to prostate cancer patients, the thesis assesses the general suitability of BNs alongside the proposed learning approaches for medical data modeling. Following comparisons with tools currently used in clinical settings and with alternative classifiers, it is shown that BNs can improve the predictive power of prostate cancer staging tools, a major concern in the field of urology

BPGA-EDA for the multi-mode resource constrained project scheduling problem.

The Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) has been of research interest for over two decades. The problem is composed of two interacting sub problems: mode assignment and activity scheduling. These problems cannot be solved in isolation because of the interaction that exists between them. Many evolutionary algorithms have been applied to this problem most commonly the Genetic Algorithm (GA). It has been common practice to improve the performance of the GA with some local search techniques. The Bi-population Genetic Algorithm (BPGA) is one of the most competitive GAs for solving the MRCPSP. In this paper, we improve the BPGA by hybridising it with an Estimation of Distribution Algorithm that focuses on improving how modes are generated. We also suggest improvement to the existing experimental methodology

RK-EDA: a novel random key based estimation of distribution algorithm.

The challenges of solving problems naturally represented as permutations by Estimation of Distribution Algorithms (EDAs) have been a recent focus of interest in the evolutionary computation community. One of the most common alternative representations for permutation based problems is the Random Key (RK), which enables the use of continuous approaches for this problem domain. However, the use of RK in EDAs have not produced competitive results to date and more recent research on permutation based EDAs have focused on creating superior algorithms with specially adapted representations. In this paper, we present RK-EDA; a novel RK based EDA that uses a cooling scheme to balance the exploration and exploitation of a search space by controlling the variance in its probabilistic model. Unlike the general performance of RK based EDAs, RK-EDA is actually competitive with the best EDAs on common permutation test problems: Flow Shop Scheduling, Linear Ordering, Quadratic Assignment, and Travelling Salesman Problems

Truck and trailer scheduling in a real world, dynamic and heterogeneous context.

We present a new variant of the Vehicle Routing Problem based on a real industrial scenario. This VRP is dynamic and heavily constrained and uses time-windows, a heterogeneous vehicle fleet and multiple types of job. A constructive solver is developed and tested using dynamic simulation of real-world data from a leading Scottish haulier. Our experiments establish the efficiency and reliability of the method for this problem. Additionally, a methodology for evaluating policy changes through simulation is presented, showing that our technique supports operations and management. We establish that fleet size can be reduced or more jobs handled by the company

A random key based estimation of distribution algorithm for the permutation flowshop scheduling problem.

Random Key (RK) is an alternative representation for permutation problems that enables application of techniques generally used for continuous optimisation. Although the benefit of RKs to permutation optimisation has been shown, its use within Estimation of Distribution Algorithms (EDAs) has been a challenge. Recent research proposing a RK-based EDA (RKEDA) has shown that RKs can produce competitive results with state of the art algorithms. Following promising results on the Permutation Flowshop Scheduling Problem, this paper presents an analysis of RK-EDA for optimising the total flow time. Experiments show that RK-EDA outperforms other permutationbased EDAs on instances of large dimensions. The difference in performance between RK-EDA and the state of the art algorithms also decreases when the problem difficulty increases

An analysis of indirect optimisation strategies for scheduling.

By incorporating domain knowledge, simple greedy procedures can be defined to generate reasonably good solutions to many optimisation problems. However, such solutions are unlikely to be optimal and their quality often depends on the way the decision variables are input to the greedy method. Indirect optimisation uses meta-heuristics to optimise the input of the greedy decoders. As the performance and the runtime differ across greedy methods and meta-heuristics, deciding how to split the computational effort between the two sides of the optimisation is not trivial and can significantly impact the search. In this paper, an artificial scheduling problem is presented along with five greedy procedures, using varying levels of domain information. A methodology to compare different indirect optimisation strategies is presented using a simple Hill Climber, a Genetic Algorithm and a population-based Local Search. By assessing all combinations of meta-heuristics and greedy procedures on a range of problem instances with different properties, experiments show that encapsulating problem knowledge within greedy decoders may not always prove successful and that simpler methods can lead to comparable results as advanced ones when combined with meta-heuristics that are adapted to the problem. However, the use of efficient greedy procedures reduces the relative difference between meta-heuristics

Estimation of distribution algorithms for the multi-mode resource constrained project scheduling problem.

Multi-Mode Resource Constrained Project Problem (MRCPSP) is a multi-component problem which combines two interacting sub-problems; activity scheduling and mode assignment. Multi-component problems have been of research interest to the evolutionary computation community as they are more complex to solve. Estimation of Distribution Algorithms (EDAs) generate solutions by sampling a probabilistic model that captures key features of good solutions. Often they can significantly improve search efficiency and solution quality. Previous research has shown that the mode assignment sub-problem can be more effectively solved with an EDA. Also, a competitive Random Key based EDA (RK-EDA) for permutation problems has recently been proposed. In this paper, activity and mode solutions are respectively generated using the RK-EDA and an integer based EDA. This approach is competitive with leading approaches of solving the MRCPSP

Performance analysis of GA and PBIL variants for real-world location-allocation problems.

The Uncapacitated Location-Allocation problem (ULAP) is a major optimisation problem concerning the determination of the optimal location of facilities and the allocation of demand to them. In this paper, we present two novel problem variants of Non-Linear ULAP motivated by a real-world problem from the telecommunication industry: Uncapacitated Location-Allocation Resilience problem (ULARP) and Uncapacitated Location-Allocation Resilience problem with Restrictions (ULARPR). Problem sizes ranging from 16 to 100 facilities by 50 to 10000 demand points are considered. To solve the problems, we explore the components and configurations of four Genetic Algorithms [1], [2], [3] and [4] selected from the ULAP literature. We aim to understand the contribution each choice makes to the GA performance and so hope to design an Optimal GA configuration for the novel problems.We also conduct comparative experiments with Population-Based Incremental Learning (PBIL) Algorithm on ULAP. We show the effectiveness of PBIL and GA with parameter set: random and heuristic initialisation, tournament and fined grained tournament selection, uniform crossover and bitflip mutation in solving the proposed problems

Machine learning for improved pathological staging of prostate cancer : A performance comparison on a range of classifiers

Peer reviewedPostprin

Génération de signaux anonymes à partir de données non anonymes par modèle de mélange linéaire local

The aim of this work is to propose a technique of anonymization through signal generation from a non-anonymous database. The adopted method consist of a statistical mixing model where each signal is considered as a mixture of its neighbors in the database. This generative model is then used to simulate a set of synthetic signals containing all the information present in the initial database. The quality of the simulated database is assessed in terms of privacy and utility criteria. The proposed approach is applied to a database of real ECG signals.L'objet de cette communication est de proposer une technique de génération de signaux synthétiques anonymes à partir d'une base de données non-anonyme. La méthode adoptée consiste à construire un modèle statistique dans lequel chaque signal de la base de données initiale est représenté comme un mélange linéaire de ses voisins. Ce modèle génératif est ensuite utilisé pour simuler un ensemble de signaux synthétiques différents de ceux de la base de données initiale mais contenant toutes les informations nécessaires pour son exploitation. La qualité de la base de données simulée est quantifiée par des mesures et d'utilité et de vie privée. Cette technique est appliquée à une base de signaux ECG réels