A Genetic Algorithm Approach for Technology Characterization

It is important for engineers to understand the capabilities and limitations of the technologies they consider for use in their systems. Several researchers have investigated approaches for modeling the capabilities of a technology with the aim of supporting the design process. In these works, the information about the physical form is typically abstracted away. However, the efficient generation of an accurate model of technical capabilities remains a challenge. Pareto frontier based methods are often used but yield results that are of limited use for subsequent decision making and analysis. Models based on parameterized Pareto frontiers—termed Technology Characterization Models (TCMs)—are much more reusable and composable. However, there exists no efficient technique for modeling the parameterized Pareto frontier. The contribution of this thesis is a new algorithm for modeling the parameterized Pareto frontier to be used as a model of the characteristics of a technology. The novelty of the algorithm lies in a new concept termed predicted dominance. The proposed algorithm uses fundamental concepts from multi-objective optimization and machine learning to generate a model of the technology frontier

Neural Architecture Search Using Genetic Algorithm for Facial Expression Recognition

Facial expression is one of the most powerful, natural, and universal signals for human beings to express emotional states and intentions. Thus, it is evident the importance of correct and innovative facial expression recognition (FER) approaches in Artificial Intelligence. The current common practice for FER is to correctly design convolutional neural networks' architectures (CNNs) using human expertise. However, finding a well-performing architecture is often a very tedious and error-prone process for deep learning researchers. Neural architecture search (NAS) is an area of growing interest as demonstrated by the large number of scientific works published in recent years thanks to the impressive results achieved in recent years. We propose a genetic algorithm approach that uses an ingenious encoding-decoding mechanism that allows to automatically evolve CNNs on FER tasks attaining high accuracy classification rates. The experimental results demonstrate that the proposed algorithm achieves the best-known results on the CK+ and FERG datasets as well as competitive results on the JAFFE dataset

Agents in a privacy-preserving world

Privacy is a fluid concept. It is both difficult to define and difficult to achieve. The large amounts of data currently available at hands of companies and administrations increase individual concerns on what is yet to be known about us. For the sake of penalisation and customisation, we often need to give up and supply information that we consider sensitive and private. Other sensitive information is inferred from information that seems harmless. Even when we explicitly require privacy and anonymity, profiling and device fingerprinting may disclose information about us leading to reidentification. Mobile devices and the internet of things make keeping our live private still more difficult. Agent technologies can play a fundamental role to provide privacy-aware solutions. Agents are inherently suitable in the heterogeneous environment in which our devices work, and we can delegate to them the task of protecting our privacy. Agents should be able to reason about our privacy requirements, and may collaborate (or not) with other agents to help us to achieve our privacy goals. We are presented in the connected world with multiple interests, profiles, and also through multiple agentified devices. We envision our agentified devices to collaborate among themselves and with other devices so that our privacy preferences are satisfied. We believe that this is an overlooked field. Our work intends to start shedding some light on the topic by outlining the requirements and challenges where agent technologies can provide a decisive role

Parametric Optimization: Applications in Systems Design

The aim of this research is to introduce the notion of parametric optimization (PO) as a useful approach for solving systems design challenges. In this research, we define PO as the process of finding the optimal solution as a function of one or more parameters. Parameters are variables that affect the optimal solution but, unlike the decision variables, are not directly controlled by the designer. The principal contributions of this research are (1) a novel formulation of the PO problem relevant to systems design, (2) a strategy for empirically assessing the performance of parametric search algorithms, (3) the development and evaluation of novel algorithms for PO, and (4) a demonstration of the use of PO for two real-world systems design challenges. The real-world demonstrations, include the design of (i) a multi-ratio vehicle transmission, and (ii) a Liquid Metal Magnetohydrodynamic Pump. A practical challenge of applying the notion PO to systems design is that existing methods are limited to problems where the models are accessible algebraic equations and single objectives. However, many challenges in systems design involve inaccessible models or are too complicated to be manipulated algebraically and have multiple objectives. If PO is to be used widely in systems design, there is a need for search methods that can approximate the solution to a general PO problem. As a step toward this goal, a strategy for performance assessment is developed. The use of the mean Hausdorff distance is proposed as a measure of solution quality for the PO problem. The mean Hausdorff distance has desirable properties from a mathematical and decision theoretic basis. Using the proposed performance assessment strategy, two algorithms for parametric optimization are evaluated, (a) p-NSGAII which is a straightforward extension of existing methods to the case with parameters, and (b) P3GA an algorithm intended to exploit the parametric structure of the problem. The results of the study indicate that a considered approach, P3GA, to the PO problem results in considerable computational advantage

Statistical Tree-based Population Seeding for Rolling Horizon EAs in General Video Game Playing

Multiple Artificial Intelligence (AI) methods have been proposed over recent years to create controllers to play multiple video games of different nature and complexity without revealing the specific mechanics of each of these games to the AI methods. In recent years, Evolutionary Algorithms (EAs) employing rolling horizon mechanisms have achieved extraordinary results in these type of problems. However, some limitations are present in Rolling Horizon EAs making it a grand challenge of AI. These limitations include the wasteful mechanism of creating a population and evolving it over a fraction of a second to propose an action to be executed by the game agent. Another limitation is to use a scalar value (fitness value) to direct evolutionary search instead of accounting for a mechanism that informs us how a particular agent behaves during the rolling horizon simulation. In this work, we address both of these issues. We introduce the use of a statistical tree that tackles the latter limitation. Furthermore, we tackle the former limitation by employing a mechanism that allows us to seed part of the population using Monte Carlo Tree Search, a method that has dominated multiple General Video Game AI competitions. We show how the proposed novel mechanism, called Statistical Tree-based Population Seeding, achieves better results compared to vanilla Rolling Horizon EAs in a set of 20 games, including 10 stochastic and 10 deterministic games

Exploration of the High Entropy Alloy Space as a Constraint Satisfaction Problem

High Entropy Alloys (HEAs), Multi-principal Component Alloys (MCA), or Compositionally Complex Alloys (CCAs) are alloys that contain multiple principal alloying elements. While many HEAs have been shown to have unique properties, their discovery has been largely done through costly and time-consuming trial-and-error approaches, with only an infinitesimally small fraction of the entire possible composition space having been explored. In this work, the exploration of the HEA composition space is framed as a Continuous Constraint Satisfaction Problem (CCSP) and solved using a novel Constraint Satisfaction Algorithm (CSA) for the rapid and robust exploration of alloy thermodynamic spaces. The algorithm is used to discover regions in the HEA Composition-Temperature space that satisfy desired phase constitution requirements. The algorithm is demonstrated against a new (TCHEA1) CALPHAD HEA thermodynamic database. The database is first validated by comparing phase stability predictions against experiments and then the CSA is deployed and tested against design tasks consisting of identifying not only single phase solid solution regions in ternary, quaternary and quinary composition spaces but also the identification of regions that are likely to yield precipitation-strengthened HEAs.Comment: 14 pages, 13 figure

On the Use of Dynamic GP Fitness Cases in Static and Dynamic Optimisation Problems

In Genetic Programming (GP), the fitness of individuals is normally computed by using a set of fitness cases (FCs). Research on the use of FCs in GP has primarily focused on how to reduce the size of these sets. However, often, only a small set of FCs is available and there is no need to reduce it. In this work, we are interested in using the whole FCs set, but rather than adopting the commonly used GP approach of presenting the entire set of FCs to the system from the beginning of the search, referred as static FCs, we allow the GP system to build it by aggregation over time, named as dynamic FCs, with the hope to make the search more amenable. Moreover, there is no study on the use of FCs in Dynamic Optimisation Problems (DOPs). To this end, we also use the Kendall Tau Distance (KTD) approach, which quantifies pairwise dissimilarities among two lists of fitness values. KTD aims to capture the degree of a change in DOPs and we use this to promote structural diversity. Results on eight symbolic regression functions indicate that both approaches are highly beneficial in GP

Multiple Interactive Outputs in a Single Tree: An Empirical Investigation

his paper describes Multiple Interactive Outputs in a Single Tree (MIOST), a new form of Genetic Programming (GP). Our approach is based on two ideas. Firstly, we have taken inspiration from graph-GP representations. With this idea we decided to explore the possibility of representing programs as graphs with oriented links. Secondly, our individuals could have more than one output. This idea was inspired on the divide and conquer principle, a program is decomposed in subprograms, and so, we are expecting to make the original problem easier by breaking down a problem into two or more sub-problems. To verify the effectiveness of our approach, we have used several evolvable hardware problems of different complexity taken from the literature. Our results indicate that our approach has a better overall performance in terms of consistency to reach feasible solutions