Loading...
 

GECCO Workshops-1 2015

GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

Full Citation in the ACM Digital Library

WORKSHOP SESSION: Genetic Improvement 2015 Workshop

Session details: Genetic Improvement 2015 Workshop

  • Langdon William B.

It is our great pleasure to welcome you to the first international workshop on the Genetic Improvement of Software -- GI-2015, held at GECCO'15. Our goal was to bring together research from across the globe to exchange ideas on using optimisation ...

GI4GI: Improving Genetic Improvement Fitness Functions

  • Harman Mark

Genetic improvement (GI) has been successfully used to optimise non-functional properties of software, such as execution time, by automatically manipulating program's source code. Measurement of non-functional properties, however, is a non-trivial task; ...

Evolutionary Approximation of Software for Embedded Systems: Median Function

  • Mrazek Vojtech

This paper deals with genetic programming-based improvement of non-functional properties of programs intended for low-cost microcontrollers. As the objective is to significantly reduce power consumption and execution time, the approximate computing ...

Genetic Improvement using Higher Order Mutation

  • Jia Yue

This paper presents a brief outline of a higher-order mutation-based framework for Genetic Improvement (GI). We argue that search-based higher-order mutation testing can be used to implement a form of genetic programming (GP) to increase the search ...

Grow and Graft a Better CUDA pknotsRG for RNA Pseudoknot Free Energy Calculation

  • Langdon William B.

Grow and graft genetic programming greatly improves GPGPU dynamic programming software for predicting the minimum binding energy for folding of RNA molecules. The parallel code inserted into the existing CUDA version of pknots was grown using a BNF ...

locoGP: Improving Performance by Genetic Programming Java Source Code

  • Cody-Kenny Brendan

We present locoGP, a Genetic Programming (GP) system written in Java for evolving Java source code. locoGP was designed to improve the performance of programs as measured in the number of operations executed. Variable test cases are used to maintain ...

Energy Optimisation via Genetic Improvement: A SBSE technique for a new era in Software Development

  • Bruce Bobby R.

The discipline of Software Engineering has arisen during a time in which developers rarely concerned themselves with the energy efficiency of their software. Due to the growth in both mobile devices and large server clusters this period is undoubtedly ...

Genetic Improvement of Energy Usage is only as Reliable as the Measurements are Accurate

  • Haraldsson Saemundur O.

Energy has recently become an objective for Genetic Improvement. Measuring software energy use is complicated which might tempt us to use simpler measurements. However if we base the GI on inaccurate measurements we can not expect improvements. This ...

Genetic Improvement for Software Product Lines: An Overview and a Roadmap

  • Lopez-Herrejon Roberto E.

Software Product Lines (SPLs) are families of related software systems that provide different combinations of features. Extensive research and application attest to the significant economical and technological benefits of employing SPL practices. ...

Embedded Dynamic Improvement

  • Burles Nathan

We discuss the useful role that can be played by a subtype of improvement programming, which we term `Embedded Dynamic Improvement'. In this approach, developer-specified variation points define the scope of improvement. A search framework is embedded ...

Removing the Kitchen Sink from Software

  • Landsborough Jason

We would all benefit if software were slimmer, thinner, and generally only did what we needed and nothing more. To this end, our research team has been exploring methods for removing unused and undesirable features from compiled programs. Our primary ...

Embedding Adaptivity in Software Systems using the ECSELR framework

  • Yeboah-Antwi Kwaku

ECSELR is an ecologically-inspired approach to software evolution that enables environmentally driven evolution at runtime in extant software systems without relying on any offline components or management. ECSELR embeds adaptation and evolution inside ...

Rethinking Genetic Improvement Programming

  • White David R.

We re-examine the central motivation behind Genetic Improvement Programming (GIP), and argue that the most important insight is the concept of applying Genetic Programming to existing software. This viewpoint allows us to make several observations about ...

Repairing COTS Router Firmware without Access to Source Code or Test Suites: A Case Study in Evolutionary Software Repair

  • Schulte Eric M.

The speed with which newly discovered software vulnerabilities are patched is a critical factor in mitigating the harm caused by subsequent exploits. Unfortunately, software vendors are often slow or unwilling to patch vulnerabilities, especially in ...

Fitness as Task-relevant Information Accumulation

  • Johnson Colin G.

"If you cannot measure it, you cannot improve it. Lord Kelvin Fitness in GP/GI is usually a short-sighted greedy fitness function counting the number of satisfied test cases (or some other score based on error). If GP/GI is to be extended to ...

WORKSHOP SESSION: SecDef'15 Workshop

Session details: SecDef'15 Workshop

  • Zincir-Heywood Nur

It is our great pleasure to welcome you to the 2015 Workshop on Genetic and Evolutionary Computation in Defense, Security, and Risk Management (SecDef'15). With the constant appearance of new threats, research in the areas of defense, security and risk ...

    Coevolutionary Agent-based Network Defense Lightweight Event System (CANDLES)

    • Rush George

    Predicting an adversary's capabilities, intentions, and probable vectors of attack is in general a complex and arduous task. Cyber space is particularly vulnerable to unforeseen attacks, as most computer networks have a large, complex, opaque attack ...

    Using Genetic Algorithms for Deadline-Constrained Monitor Selection in Dynamic Computer Networks

    • Mueller-Bady Robin

    In this paper we address the problem of selecting a minimal number of optimally positioned monitors for capturing network traffic in dynamic computer network environments. Requirements of computer network monitoring change frequently, e.g., due to ...

    A Hybrid Matheuristic Approach for Designing Reliable Wireless Multimedia Sensor Networks

    • Ozkan Omer

    One of the most important design considerations for Wireless Multimedia Sensor Networks (WMSNs) is the reliability which involves connectivity and coverage issues with sensor and relay node deployment strategies that affects the coverage performance of ...

    A Preliminary Investigation on the Identification of Peer to Peer Network Applications

    • Bozdogan Can

    Identification of P2P (peer to peer) applications inside network traffic plays an important role for route provisioning, traffic policing, flow prioritization, network service pricing, network capacity planning and network resource management. ...

    Evolutionary Dynamic Optimization Techniques for Marine Contamination Problem

    • Altin Lokman

    Marine pollution is the release of by-products that cause harm to natural marine ecosystems and one of the most important sources is the discharge of oil, ballast water from vessels. If the relevant technology is not available, alternative way to ...

    Botnet Detection System Analysis on the Effect of Botnet Evolution and Feature Representation

    • Haddadi Fariba

    Botnets are known as one of the main destructive threats that have been active since 2003 in various forms. The ability to upgrade the structure and algorithms on the fly is part of what causes botnets to survive for more than a decade. Hence, one of ...

    WORKSHOP SESSION: ECCSB'15 Workshop

    Session details: ECCSB'15 Workshop

    • Reyes José Santos

    It is our great pleasure to welcome you to the 2015 ACM Workshop Evolutionary Computation in Computational Structural Biology, associated with GECCO 2015, the largest international conference in the field of genetic and evolutionary computation (with ...

    MeGASS: Multi-Objective Genetic Active Site Search

    • Izidoro Sandro

    Active sites are regions in the enzyme surface designed to interact with other molecules. Given their importance to enzyme function, active site amino acids are more conserved during evolution than the whole sequence, and can be a useful source of ...

    Combination of Differential Evolution and Fragment-based Replacements for Protein Structure Prediction

    • Varela Daniel

    In this work Differential Evolution (DE) was combined with fragment replacement for improving the search of protein structure conformations with minimum energy. The Rosetta environment was used, employing some of its phases for the ab initio prediction ...

    NK Landscape Instances Mimicking the Protein Inverse Folding Problem Towards Future Benchmarks

    • Nielsen Sune S.

    This paper introduces two new nominal NK Landscape model instances designed to mimic the properties of one challenging optimisation problem from biology: the Inverse Folding Problem (IFP), here focusing on a simpler secondary structure version. Through ...

    Mapping Multiple Minima in Protein Energy Landscapes with Evolutionary Algorithms

    • Sapin Emmanuel

    Many proteins involved in human proteinopathies exhibit complex energy landscapes with multiple thermodynamically-stable and semi-stable structural states. Landscape reconstruction is crucial to understanding functional modulations, but one is ...

    An Experimental Analysis of the Performance of SideChain Packing Algorithms

    • Brizuela Carlos

    This paper presents a brief description of the protein side chain packing problem (PSCPP) and a performance assessment, on this problem, of three state-of-the-art algorithms: SCWRL4, OPUS-Rota, and CIS-RR. In order to perform a fair comparison, the ...

    Using Machine Learning to Explore the Relevance of Local and Global Features During Conformational Search in Rosetta

    • Garza-Fabre Mario

    Our ongoing work focuses on improvements to the exploration behaviour of heuristic search techniques in fragment-assembly methods for protein structure prediction. Analysing and improving exploration in fragment-assembly can be difficult due to the ...

    WORKSHOP SESSION: VizGEC'15 Workshop

    Session details: VizGEC'15 Workshop

    • Walker David

    We are pleased to welcome you to the sixth workshop on visualisation in genetic and evolutionary computation, held at GECCO in Madrid 2015. This workshop continues the tradition of showcasing the latest in visualisation work in this field.

    Following a ...

    Spatial and Temporal Visualisation of Evolutionary Algorithm Decisions in Water Distribution Network Optimisation

    • Keedwell Edward

    Much research has been conducted into the visualisation of objective space and decision space landscapes. This work moves away from this and investigates a 3D interactive method for linking EA decisions through time with the design of engineering ...

    ELICIT: Evolutionary Computation Visualization

    • Cruz António

    ELICIT is a generic tool that enables the visual exploration of evolutionary computation algorithms. It is characterized by the use of simple visual elements to represent information and by the adoption of interactive techniques which allow the ...

    Using Particle Swarm Large-scale Optimization to Improve Sampling-based Image Matting

    • Liang Lv

    Sampling-based image matting is an important basic operator of image processing. The matting results are depended on the quality of sample selection. The sample selection produces a pair of samples for each pixel to detect whether the pixel is in the ...

    Visualising Multi-objective Populations with Treemaps

    • Walker David J.

    Visualising populations of solutions is an important aspect of evolutionary computation (EC), allowing an algorithm user to evaluate the performance of an algorithm and a decision maker to understand the solution set from which they must choose an ...

    WORKSHOP SESSION: IWERML'15 Workshop

    Session details: IWERML'15 Workshop

    • Kuber Karthik

    It is our great pleasure to welcome you to the International Workshop on Evolutionary Rule-Based Machine Learning Workshop held in conjunction with GECCO 2015. In previous years, this workshop has been hosted as the International Workshop on Learning ...

    A Novel Representation of Classifier Conditions Named Sensory Tag for the XCS in Multistep Problems

    • Chen Liang-Yu

    Dynamically adding sensors to the Extended Classifier System (XCS) during its learning process in multistep problems has been demonstrated feasible by using messy coding (XCSm) and s-expressions (XCSL) as the representation of classifier conditions. ...

    The Relationship Between (Un)Fractured Problems and Division of Input Space

    • Vargas Danilo Vasconcellos

    Problems can be categorized as fractured or unfractured ones. A different set of characteristics are needed for learning algorithms to solve each of these two types of problems. However, the exact characteristics needed to solve each type are unclear. ...

    A Lexicographic Multi-Objective Genetic Algorithm for Multi-Label Correlation Based Feature Selection

    • Jungjit Suwimol

    This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Multi-Label Correlation-based Feature Selection (LexGA-ML-CFS), which is an extension of the previous single-objective Genetic Algorithm for Multi-label Correlation-based ...

    Metaheuristics based on Clustering in a Holonic Multiagent Model for the Flexible Job Shop Problem

    • Nouri Houssem Eddine

    The Flexible Job Shop scheduling Problem (FJSP) is a generalization of the classical Job Shop scheduling Problem (JSP) allowing to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two ...

    Discovering Regression Rules with Ant Colony Optimization

    • Brookhouse James

    The majority of Ant Colony Optimization (ACO) algorithms for data mining have dealt with classification or clustering problems. Regression remains an unexplored research area to the best of our knowledge. This paper proposes a new ACO algorithm that ...

    An Evolutionary Missing Data Imputation Method for Pattern Classification

    • Lobato Fábio M.F.

    Data analysis plays an important role in our Information Era; however, most of statistical and machine learning algorithms were not developed to tackle the ubiquitous issue of missing values. In pattern classification, several strategies have been ...

    Socially Guided XCS: Using Teaching Signals to Boost Learning

    • Najar Anis

    In this paper, we show how we can improve task learning by using social interaction to guide the learning process of a robot, in a Human-Robot Interaction scenario. We introduce a novel method that simultaneously learns a social reward function on the ...

    Continuous Endpoint Data Mining with ExSTraCS: A Supervised Learning Classifier System

    • Urbanowicz Ryan

    ExSTraCS is a powerful Michigan-style learning classifier system (LCS) that was developed for classification, prediction, modeling, and knowledge discovery in complex and/or heterogeneous supervised learning problems with clean or noisy signals. To date,...

    Back to the Future: Learning Classifier Systems as Cognitive Systems

    • Browne Will n.

    Cognitive System One (CS-1) was the first Learning Classifier System (LCSs) that was introduced nearly four decades ago. Subsequently, LCSs have been substantially developed to be powerful classification systems that have unique abilities in terms of ...

    A Potential of Evolutionary Rule-based Machine Learning for Real World Applications

    • Takadama Keiki

    This paper explores a potential of Evolutionary Rule-based Machine Learning (ERML) by showing how ERML succeeds in real world applications. Generally, ERML is defined as a method that integrates (rule-based) machine learning with evolutionary ...