High performance genetic programming on GPU

The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Programming (GP) on Graphics Processing Units (GPUs). Our work focuses on the possibilities offered by Nvidia G80 GPUs when pro-grammed in the CUDA language. We compare two par-allelization schemes that evaluate several GP programs in parallel. We show that the fine grain distribution of compu-tations over the elementary processors greatly impacts per-formances. We also present memory and representation op-timizations that further enhance computation speed, up to 2.8 billion GP operations per second. The code has been developed with the well known ECJ library

A multi-population hybrid Genetic Programming System

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn the last few years, geometric semantic genetic programming has incremented its popularity, obtaining interesting results on several real life applications. Nevertheless, the large size of the solutions generated by geometric semantic genetic programming is still an issue, in particular for those applications in which reading and interpreting the final solution is desirable. In this thesis, a new parallel and distributed genetic programming system is introduced with the objective of mitigating this drawback. The proposed system (called MPHGP, which stands for Multi-Population Hybrid Genetic Programming) is composed by two types of subpopulations, one of which runs geometric semantic genetic programming, while the other runs a standard multi-objective genetic programming algorithm that optimizes, at the same time, fitness and size of solutions. The two subpopulations evolve independently and in parallel, exchanging individuals at prefixed synchronization instants. The presented experimental results, obtained on five real-life symbolic regression applications, suggest that MPHGP is able to find solutions that are comparable, or even better, than the ones found by geometric semantic genetic programming, both on training and on unseen testing data. At the same time, MPHGP is also able to find solutions that are significantly smaller than the ones found by geometric semantic genetic programming

A Parallel Evolutionary Algorithm to Optimize Dynamic Memory Managers in Embedded Systems

For the last 30 years, several dynamic memory managers (DMMs) have been proposed. Such DMMs include first fit, best fit, segregated fit and buddy systems. Since the performance, memory usage and energy consumption of each DMM differs, software engineers often face difficult choices in selecting the most suitable approach for their applications. This issue has special impact in the field of portable consumer embedded systems, that must execute a limited amount of multimedia applications (e.g., 3D games, video players, signal processing software, etc.), demanding high performance and extensive memory usage at a low energy consumption. Recently, we have developed a novel methodology based on genetic programming to automatically design custom DMMs, optimizing performance, memory usage and energy consumption. However, although this process is automatic and faster than state-of-the-art optimizations, it demands intensive computation, resulting in a time-consuming process. Thus, parallel processing can be very useful to enable to explore more solutions spending the same time, as well as to implement new algorithms. In this paper we present a novel parallel evolutionary algorithm for DMMs optimization in embedded systems, based on the Discrete Event Specification (DEVS) formalism over a Service Oriented Architecture (SOA) framework. Parallelism significantly improves the performance of the sequential exploration algorithm. On the one hand, when the number of generations are the same in both approaches, our parallel optimization framework is able to reach a speed-up of 86.40% when compared with other state-of-the-art approaches. On the other, it improves the global quality (i.e., level of performance, low memory usage and low energy consumption) of the final DMM obtained in a 36.36% with respect to two well-known general-purpose DMMs and two state-of-the-art optimization methodologies

An Analysis of Diversity in Genetic Programming

Genetic programming is a metaheuristic search method that uses a population of variable-length computer programs and a search strategy based on biological evolution. The idea of automatic programming has long been a goal of artificial intelligence, and genetic programming presents an intuitive method for automatically evolving programs. However, this method is not without some potential drawbacks. Search using procedural representations can be complex and inefficient. In addition, variable sized solutions can become unnecessarily large and difficult to interpret. The goal of this thesis is to understand the dynamics of genetic programming that encourages efficient and effective search. Toward this goal, the research focuses on an important property of genetic programming search: the population. The population is related to many key aspects of the genetic programming algorithm. In this programme of research, diversity is used to describe and analyse populations and their effect on search. A series of empirical investigations are carried out to better understand the genetic programming algorithm. The research begins by studying the relationship between diversity and search. The effect of increased population diversity and a metaphor of search are then examined. This is followed by an investigation into the phenomenon of increased solution size and problem difficulty. The research concludes by examining the role of diverse individuals, particularly the ability of diverse individuals to affect the search process and ways of improving the genetic programming algorithm. This thesis makes the following contributions: (1) An analysis shows the complexity of the issues of diversity and the relationship between diversity and fitness, (2) The genetic programming search process is characterised by using the concept of genetic lineages and the sampling of structures and behaviours, (3) A causal model of the varied rates of solution size increase is presented, (4) A new, tunable problem demonstrates the contribution of different population members during search, and (5) An island model is proposed to improve the search by speciating dissimilar individuals into better-suited environments. Currently, genetic programming is applied to a wide range of problems under many varied contexts. From artificial intelligence to operations research, the results presented in this thesis will benefit population-based search methods, methods based on the concepts of evolution and search methods using variable-length representations