Code Building Genetic Programming

In recent years the field of genetic programming has made significant advances towards automatic programming. Research and development of contemporary program synthesis methods, such as PushGP and Grammar Guided Genetic Programming, can produce programs that solve problems typically assigned in introductory academic settings. These problems focus on a narrow, predetermined set of simple data structures, basic control flow patterns, and primitive, non-overlapping data types (without, for example, inheritance or composite types). Few, if any, genetic programming methods for program synthesis have convincingly demonstrated the capability of synthesizing programs that use arbitrary data types, data structures, and specifications that are drawn from existing codebases. In this paper, we introduce Code Building Genetic Programming (CBGP) as a framework within which this can be done, by leveraging programming language features such as reflection and first-class specifications. CBGP produces a computational graph that can be executed or translated into source code of a host language. To demonstrate the novel capabilities of CBGP, we present results on new benchmarks that use non-primitive, polymorphic data types as well as some standard program synthesis benchmarks.Comment: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, Genetic Programming Trac

Evolution of Coordination in Social Networks: A Numerical Study

Coordination games are important to explain efficient and desirable social behavior. Here we study these games by extensive numerical simulation on networked social structures using an evolutionary approach. We show that local network effects may promote selection of efficient equilibria in both pure and general coordination games and may explain social polarization. These results are put into perspective with respect to known theoretical results. The main insight we obtain is that clustering, and especially community structure in social networks has a positive role in promoting socially efficient outcomes.Comment: preprint submitted to IJMP

Valuing Biodiversity from an Economic Perspective: AUnified Economic, Ecological and Genetic Approach

We develop a conceptual framework for valuing biodiversity from an economic perspective. We consider biodiversity important because of a number of characteristics or services that it provides or enhances. We argue for a dynamic economic welfare measure of biodiversity that complements the existing literature on benefit-cost approaches and genetic distance/phylogenic tree approaches, which to date have been more static. Using a unified model of optimal economic management of an ecosystem under ecological and genetic constraints, we identify gains realized by management policies leading to a more diverse system, using the Bellman state valuation function of the problem. We show that a more diverse system could attain a higher value even though the genetic distance of the species in the more diverse system could be almost zero. We relate this endogenous measure of the biodiversity value to ecologically/biologically oriented biodiversity metrics (species richness, Shannon or Simpson indices).

Valuing biodiversity from an economic perspective : a unified economic, ecological and genetic approach

biodiversionoptimization;ecology

Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations

We propose Paraiso, a domain specific language embedded in functional programming language Haskell, for automated tuning of explicit solvers of partial differential equations (PDEs) on GPUs as well as multicore CPUs. In Paraiso, one can describe PDE solving algorithms succinctly using tensor equations notation. Hydrodynamic properties, interpolation methods and other building blocks are described in abstract, modular, re-usable and combinable forms, which lets us generate versatile solvers from little set of Paraiso source codes. We demonstrate Paraiso by implementing a compressive hydrodynamics solver. A single source code less than 500 lines can be used to generate solvers of arbitrary dimensions, for both multicore CPUs and GPUs. We demonstrate both manual annotation based tuning and evolutionary computing based automated tuning of the program.Comment: 52 pages, 14 figures, accepted for publications in Computational Science and Discover

Metamorphic Code Generation from LLVM IR Bytecode

Metamorphic software changes its internal structure across generations with its functionality remaining unchanged. Metamorphism has been employed by malware writers as a means of evading signature detection and other advanced detection strate- gies. However, code morphing also has potential security benefits, since it increases the “genetic diversity” of software. In this research, we have created a metamorphic code generator within the LLVM compiler framework. LLVM is a three-phase compiler that supports multiple source languages and target architectures. It uses a common intermediate representation (IR) bytecode in its optimizer. Consequently, any supported high-level programming language can be transformed to this IR bytecode as part of the LLVM compila- tion process. Our metamorphic generator functions at the IR bytecode level, which provides many advantages over previously developed metamorphic generators. The morphing techniques that we employ include dead code insertion—where the dead code is actually executed within the morphed code—and subroutine permutation. We have tested the effectiveness of our code morphing using hidden Markov model analysis

Towards the Conceptualization of Refinement Typed Genetic Programming

Tese de mestrado, Engenharia Informática (Engenharia de Software) Universidade de Lisboa, Faculdade de Ciências, 2020The Genetic Programming (GP) approaches typically have difficulties dealing with the large search space as the number of language components grows. The increasing number of components leads to amore extensive search space and lengthens the time required to find a fitting solution. Strongly Typed Genetic Programming (STGP) tries to reduce the search space using the programming language type system, only allowing typesafe programs to be generated. Grammar Guided Genetic Programming (GGGP) allows the user to specify the program’s structure through grammar, reducing the number of combinations between the language components. However, the STGP restriction of the search space is still not capable of holding the increasing number of synthesis components, and the GGGP approach is arguably usable since it requires the user to create not only a parser and interpreter for the generated expressions from the grammar, but also all the functions existing in the grammar. This work proposes Refinement Typed Genetic Programming (RTGP), a hybrid approach between STGP and RTGP, which uses refinement types to reduce the search space while maintaining the language usability properties. This work introduces the ÆON programming language, which allows the partial or total synthesis of refinement typed programs using genetic programming. The potential of RTGP is presented with the usability arguments on two use cases against GGGP and the creation of a prototype propertybased verification tool, pyCheck, proof of RTGPs components versatility

ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special track at WSTST 2005, Muroran, JAPA