Achiya Elyasaf

GP-rush: using genetic programming to evolve solvers for the rush hour puzzle

We evolve heuristics to guide IDA* search for the 6x6 and 8x8 versions of the Rush Hour puzzle, a PSPACE-Complete problem, for which no efficient solver has yet been reported. No effective heuristic functions are known for this domain, and--before applying any evolutionary thinking--we first devise several novel heuristic measures, which improve (non-evolutionary) search for some instances, but hinder search substantially for many other instances. We then turn to genetic programming (GP) and find that evolution proves immensely efficacious, managing to combine heuristics of such highly variable utility into composites that are nearly always beneficial, and far better than each separate component. GP is thus able to beat both the human player of the game and also the human designers of heuristics.

Evolutionary Design of FreeCell Solvers

In this paper, we evolve heuristics to guide staged deepening search for the hard game of FreeCell, obtaining top-notch solvers for this human-challenging puzzle. We first devise several novel heuristic measures using minimal domain knowledge and then use them as building blocks in two evolutionary setups involving a standard genetic algorithm and policy-based, genetic programming. Our evolved solvers outperform the best FreeCell solver to date by three distinct measures: 1) number of search nodes is reduced by over 78%; 2) time to solution is reduced by over 94%; and 3) average solution length is reduced by over 30%. Our top solver is the best published FreeCell player to date, solving 99.65% of the standard Microsoft 32 K problem set. Moreover, it is able to convincingly beat high-ranking human players.

GA-FreeCell: evolving solvers for the game of FreeCell

We evolve heuristics to guide staged deepening search for the hard game of FreeCell, obtaining top-notch solvers for this NP-Complete, human-challenging puzzle. We first devise several novel heuristic measures and then employ a Hillis-style coevolutionary genetic algorithm to find efficient combinations of these heuristics. Our results significantly surpass the best published solver to date by three distinct measures: 1) Number of search nodes is reduced by 87%; 2) time to solution is reduced by 93%; and 3) average solution length is reduced by 41%. Our top solver is the best published FreeCell player to date, solving 98% of the standard Microsoft 32K problem set, and also able to beat high-ranking human players.

Software review: the HeuristicLab framework

HeuristicLab homepage: http://dev.heuristiclab.com Computer scientists often find themselves debating whether they should use (and extend) an existing system or develop their own. The onerous task of learning how to operate an existing system can be a severe overhead when all one wishes is to conduct a small experiment. However, especially where extended experiments are to be conducted, in the long run, a system that is extensible, flexible, modular, and usable, can save valuable time. HeuristicLab [6, 7] is a graphical user interface (GUI) based framework for heuristic and evolutionary algorithms, designed for ease of use. It offers a plugin-based architecture (which enables users to add custom extensions without knowing the whole of the source code), a domain-independent model to represent arbitrary search algorithms, support for graphical user interfaces, and the ability to accommodate parallel algorithms. HeuristicLab has been under development since 2002 by members of the Heuristic and Evolutionary Algorithms Laboratory (HEAL) [2], at the Upper Austria University of Applied Sciences. The team now includes six dedicated developers, who publish new versions with bug fixes and new features in a frequent, timely fashion. Additionally there are three major version releases per year. Thus, any reported bug is usually fixed almost immediately. Similarly many requested features are quickly implemented. HeuristicLab is downloaded about 70 times a week, and the number of papers citing the system is rapidly increasing. HeuristicLab

More or Less? Two Approaches to Evolving Game-Playing Strategies

We present two opposing approaches to the evolution of game strategies, one wherein a minimal amount of domain expertise is injected into the process, the other infusing the evolutionary setup with expertise in the form of domain heuristics. We show that the first approach works well for several popular board games, while the second produces top-notch solvers for the hard game of FreeCell.

Learning Heuristics for Mining RNA Sequence-Structure Motifs

The computational identification of conserved motifs in RNA molecules is a major—yet largely unsolved—problem. Structural conservation serves as strong evidence for important RNA functionality. Thus, comparative structure analysis is the gold standard for the discovery and interpretation of functional RNAs.In this paper we focus on one of the functional RNA motif types, sequence-structure motifs in RNA molecules, which marks the molecule as targets to be recognized by other molecules.We present a new approach for the detection of RNA structure (including pseudoknots), which is conserved among a set of unaligned RNA sequences. Our method extends previous approaches for this problem, which were based on first identifying conserved stems and then assembling them into complex structural motifs. The novelty of our approach is in simultaneously preforming both the identification and the assembly of these stems. We believe this novel unified approach offers a more informative model for deciphering the evolution of functional RNAs, where the sets of stems comprising a conserved motif co-evolve as a correlated functional unit.Since the task of mining RNA sequence-structure motifs can be addressed by solving the maximum weighted clique problem in an n-partite graph, we translate the maximum weighted clique problem into a state graph. Then, we gather and define domain knowledge and low-level heuristics for this domain. Finally, we learn hyper-heuristics for this domain, which can be used with heuristic search algorithms (e.g., A*, IDA*) for the mining task.The hyper-heuristics are evolved using HH-Evolver, a tool for domain-specific, hyper-heuristic evolution. Our approach is designed to overcome the computational limitations of current algorithms, and to remove the necessity of previous assumptions that were used for sparsifying the graph.This is still work in progress and as yet we have no results to report. However, given the interest in the methodology and its previous success in other domains we are hopeful that these shall be forthcoming soon.

HH-evolver: a system for domain-specific, hyper-heuristic evolution

We present HH-Evolver, a tool for domain-specific, hyper-heuristic evolution. HH-Evolver automates the design of domain-specific heuristics for planning domains. Hyper-heuristics generated by our tool can be used with combinatorial search algorithms such as A* and IDA* for solving problems of a given domain. HH-Evolver has a rich GUI that enables easy operation, including: running experiments in parallel, pausing and resuming experiments, and saving them and analyzing the results. Implementing new domains and heuristics with HH-Evolver is easily accomplished.

Evolving Artificial General Intelligence for Video Game Controllers

The General Video Game Playing Competition (GVGAI) defines a challenge of creating controllers for general video game playing, a testbed—as it were—for examining the issue of artificial general intelligence. We develop herein a game controller that mimics human learning behavior, focusing on the ability to generalize from experience and diminish learning time as new games present themselves. We use genetic programming to evolve hyper-heuristic-based general players. Our results show the effectiveness of evolution in meeting the generality challenge.

Lunch isn't free --- but cells are: evolving FreeCell players

The application of computational intelligence techniques within the vast domain of games has been increasing at a breath-taking speed. Over the past several years our research group has produced a plethora of results in numerous games of different natures, evidencing the success and efficiency of evolutionary algorithms in general --- and genetic programming in particular --- at producing top-notch, human-competitive game strategies. Herein, we describe our study of the game of FreeCell, which produced two Gold Humie Awards. Our top evolved FreeCell player is the best published player to date, able to convincingly beat high-ranking human players.

A heuristiclab evolutionary algorithm for FINCH

We present a HeuristicLab plugin for FINCH. FINCH (Fertile Darwinian Bytecode Harvester) is a system designed to evolutionarily improve actual, extant software, which was not intentionally written for the purpose of serving as a GP representation in particular, nor for evolution in general. This is in contrast to existing work that uses restricted subsets of the Java bytecode instruction set as a representation language for individuals in genetic programming. The ability to evolve Java programs will hopefully lead to a valuable new tool in the software engineers toolkit.