A simple strategy for maintaining diversity and reducing crowding in differential evolution

Differential evolution (DE) is a widely-effective population-based continuous optimiser that requires convergence to automatically scale its moves. However, once its population has begun to converge its ability to conduct global search is diminished, as the difference vectors used to generate new solutions are derived from the current population members' positions. In multi-modal search spaces DE may converge too rapidly, i.e., before adequately exploring the search space to identify the best region(s) in which to conduct its finer-grained search. Traditional crowding or niching techniques can be computationally costly or fail to compare new solutions with the most appropriate existing population member. This paper proposes a simple intervention strategy that compares each new solution with the population member it is most likely to be near, and prevents those moves that are below a threshold that decreases over the algorithm's run, allowing the algorithm to ultimately converge. Comparisons with a standard DE algorithm on a number of multi-modal problems indicate that the proposed technique can achieve real and sizable improvements.IEEE Computational Intelligence Societ

Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing

This work presents a new algorithm called evolutionary exploration of augmenting convolutional topologies (EXACT), which is capable of evolving the structure of convolutional neural networks (CNNs). EXACT is in part modeled after the neuroevolution of augmenting topologies (NEAT) algorithm, with notable exceptions to allow it to scale to large scale distributed computing environments and evolve networks with convolutional filters. In addition to multithreaded and MPI versions, EXACT has been implemented as part of a BOINC volunteer computing project, allowing large scale evolution. During a period of two months, over 4,500 volunteered computers on the Citizen Science Grid trained over 120,000 CNNs and evolved networks reaching 98.32% test data accuracy on the MNIST handwritten digits dataset. These results are even stronger as the backpropagation strategy used to train the CNNs was fairly rudimentary (ReLU units, L2 regularization and Nesterov momentum) and these were initial test runs done without refinement of the backpropagation hyperparameters. Further, the EXACT evolutionary strategy is independent of the method used to train the CNNs, so they could be further improved by advanced techniques like elastic distortions, pretraining and dropout. The evolved networks are also quite interesting, showing "organic" structures and significant differences from standard human designed architectures.Comment: 17 pages, 13 figures. Submitted to the 2017 Genetic and Evolutionary Computation Conference (GECCO 2017

Knowledge creation and visualisation by using trade-off curves to enable set-based concurrent engineering

The increased international competition forces companies to sustain and improve market share through the production of a high quality product in a cost effective manner and in a shorter time. Set‑based concurrent engineering (SBCE), which is a core element of lean product development approach, has got the potential to decrease time‑to‑market as well as enhance product innovation to be produced in good quality and cost effective manner. A knowledge‑based environment is one of the important requ irements for a successful SBCE implementation. One way to provide this environment is the use of trade‑off curves (ToC). ToC is a tool to create and visualise knowledge in the way to understand the relationships between various conflicting design parame ters to each other. This paper presents an overview of different types of ToCs and the role of knowledge‑based ToCs in SBCE by employing an extensive literature review and industrial field study. It then proposes a process of generating and using knowledg e‑based ToCs in order to create and visualise knowledge to enable the following key SBCE activities: (1) Identify the feasible design space, (2) Generate set of conceptual design solutions, (3) Compare design solutions, (4) Narrow down the design sets, (5) Achieve final optimal design solution. Finally a hypothetical example of a car seat structure is presented in order to provide a better understanding of using ToCs. This example shows that ToCs are effective tools to be used as a knowledge sou rce at the early stages of product development process

Is protein folding problem really a NP-complete one ? First investigations

To determine the 3D conformation of proteins is a necessity to understand their functions or interactions with other molecules. It is commonly admitted that, when proteins fold from their primary linear structures to their final 3D conformations, they tend to choose the ones that minimize their free energy. To find the 3D conformation of a protein knowing its amino acid sequence, bioinformaticians use various models of different resolutions and artificial intelligence tools, as the protein folding prediction problem is a NP complete one. More precisely, to determine the backbone structure of the protein using the low resolution models (2D HP square and 3D HP cubic), by finding the conformation that minimize free energy, is intractable exactly. Both the proof of NP-completeness and the 2D prediction consider that acceptable conformations have to satisfy a self-avoiding walk (SAW) requirement, as two different amino acids cannot occupy a same position in the lattice. It is shown in this document that the SAW requirement considered when proving NP-completeness is different from the SAW requirement used in various prediction programs, and that they are different from the real biological requirement. Indeed, the proof of NP completeness and the predictions in silico consider conformations that are not possible in practice. Consequences of this fact are investigated in this research work.Comment: Submitted to Journal of Bioinformatics and Computational Biology, under revie

Investigating the Effects of Learning Speeds on Xpilot Agent Evolution

In this paper we present a comparison of the effects of varying play speeds on a genetic algorithm in the space combat game Xpilot. Xpilot-AI, an Xpilot add-on designed for testing learning systems, is used to evolve the controller for an Xpilot combat agent at varying frames per second to determine an optimal speed for learning. The controller is a rule-based system modified to work with a genetic algorithm that learns numeric parameters for the agent’s rule base. The goal of this research is to increase the quality and speed of standard learning algorithms in Xpilot as well as determine a suitable speed for employing Punctuated Anytime Learning (PAL) in the Xpilot-AI environment. PAL is the learning component of an overall system of autonomous agent control with real-time learning

Local Optima Network Analysis for MAX-SAT

Local Optima Networks (LONs) are a valuable tool to understand fitness landscapes of optimization problems observed from the perspective of a search algorithm. Local optima of the optimization problem are linked by an edge in LONs when an operation in the search algorithm allows one of them to be reached from the other. Previous work analyzed several combinatorial optimization problems using LONs and provided a visual guide to understand why the instances are difficult or easy for the search algorithms. In this work we analyze for the first time the MAX-SAT problem. Given a Boolean formula in Conjunctive Normal Form, the goal of the MAX-SAT problem is to find an assignment maximizing the number of satistified clauses. Several random and industrial instances of MAX-SAT are analyzed using Iterated Local Search to sample the search space.Universidad de Málaga, Campus de Excelencia International Andalucía Tech. Universidad de Stirling, Reino Unido. Ministerio de Economía y Competitividad y FEDER (proyecto TIN2017-88213-R)

Fitness Biasing for Evolving an Xpilot Combat Agent

In this paper we present an application of Fitness Biasing, a type of Punctuated Anytime Learning, for learning autonomous agents in the space combat game Xpilot. Fitness Biasing was originally developed as a means of linking the model to the actual robot in evolutionary robotics. We use fitness biasing with a standard genetic algorithm to learn control programs for a video game agent in real-time. Xpilot-AI, an Xpilot add-on designed for testing learning systems, is used to evolve the controller in the background while periodic checks in normal game play are used to compensate for errors produced by running the system at a high frame rate. The resultant learned controllers are comparable to our best hand-coded Xpilot-AI bots, display complex behavior that resemble human strategies, and are capable of adapting to a changing enemy in real-time