11,367 research outputs found
Explicit filtering of building blocks for genetic algorithms
Genetic algorithms are often applied to building block problems. We have developed a simple filtering algorithm that can locate building blocks within a bit-string, and does not make assumptions regarding the linkage of the bits. A comparison between the filtering algorithm and genetic algorithms reveals some interesting insights, and we discuss how the filtering algorithm can be used to build a powerful hybrid genetic algorithm
Building block filtering and mixing
A three-stage evolutionary method, the BBF-GA is introduced. BBF-GA is an acronym for building block filtering genetic algorithm. During the first stage, an ensemble of fast evolutionary algorithms is used to explore the search space. The best individual found by each of these evolutionary algorithms is propagated to the next phase. During the second stage, building block filtering is used to extract the essential parts of each of these local optimal strings, and masks these essential parts. During the third stage, a single evolutionary algorithm is used to find the global optimum by recombining the masked strings. For this purpose we use a special recombination operator that exploits the information stored in the masks. Given an appropriate basis, such that partial solutions can be discovered and evaluated in parallel and be combined afterwards, a recombination-based evolutionary algorithm can be very efficient. Therefore, learning of the structure of problem-spaces is important to make a more efficient recombination possible. The BBF-GA is a first step along this line for binary search spaces and problems that adhere to the building block hypothesis
Analysis of Linkage-Friendly Genetic Algorithms
Evolutionary algorithms (EAs) are stochastic population-based algorithms inspired by the natural processes of selection, mutation, and recombination. EAs are often employed as optimum seeking techniques. A formal framework for EAs is proposed, in which evolutionary operators are viewed as mappings from parameter spaces to spaces of random functions. Formal definitions within this framework capture the distinguishing characteristics of the classes of recombination, mutation, and selection operators. EAs which use strictly invariant selection operators and order invariant representation schemes comprise the class of linkage-friendly genetic algorithms (lfGAs). Fast messy genetic algorithms (fmGAs) are lfGAs which use binary tournament selection (BTS) with thresholding, periodic filtering of a fixed number of randomly selected genes from each individual, and generalized single-point crossover. Probabilistic variants of thresholding and filtering are proposed. EAs using the probabilistic operators are generalized fmGAs (gfmGAs). A dynamical systems model of lfGAs is developed which permits prediction of expected effectiveness. BTS with probabilistic thresholding is modeled at various levels of abstraction as a Markov chain. Transitions at the most detailed level involve decisions between classes of individuals. The probability of correct decision making is related to appropriate maximal order statistics, the distributions of which are obtained. Existing filtering models are extended to include probabilistic individual lengths. Sensitivity of lfGA effectiveness to exogenous parameters limits practical applications. The lfGA parameter selection problem is formally posed as a constrained optimization problem in which the cost functional is related to expected effectiveness. Kuhn-Tucker conditions for the optimality of gfmGA parameters are derived
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
Optimization of Heterogeneous UAV Communications Using the Multiobjective Quadratic Assignment Problem
The Air Force has placed a high priority on developing new and innovative ways to use Unmanned Aerial Vehicles (UAVs). The Defense Advanced Research Projects Agency (DARPA) currently funds many projects that deal with the advancement of UAV research. The ultimate goal of the Air Force is to use UAVs in operations that are highly dangerous to pilots, mainly the suppression of enemy air defenses (SEAD). With this goal in mind, formation structuring of autonomous or semi-autonomous UAVs is of future importance. This particular research investigates the optimization of heterogeneous UAV multi-channel communications in formation. The problem maps to the multiobjective Quadratic Assignment Problem (mQAP). Optimization of this problem is done through the use of a Multiobjective Evolutionary Algorithm (MOEA) called the Multiobjective Messy Genetic Algorithm - II (MOMGA-II). Experimentation validates the attainment of an acceptable Pareto Front for a variety of mQAP benchmarks. It was observed that building block size can affect the location vectors along the current Pareto Front. The competitive templates used during testing perform best when they are randomized before each building block size evaluation. This tuning of the MOMGA-II parameters creates a more effective algorithm for the variety of mQAP benchmarks, when compared to the initial experiments. Thus this algorithmic approach would be useful for Air Force decision makers in determining the placement of UAVs in formations
A method for generating realistic correlation matrices
Simulating sample correlation matrices is important in many areas of
statistics. Approaches such as generating Gaussian data and finding their
sample correlation matrix or generating random uniform deviates as
pairwise correlations both have drawbacks. We develop an algorithm for adding
noise, in a highly controlled manner, to general correlation matrices. In many
instances, our method yields results which are superior to those obtained by
simply simulating Gaussian data. Moreover, we demonstrate how our general
algorithm can be tailored to a number of different correlation models. Using
our results with a few different applications, we show that simulating
correlation matrices can help assess statistical methodology.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS638 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Searching for invariants using genetic programming and mutation testing
Invariants are concise and useful descriptions of a program's behaviour. As most programs are not annotated with invariants, previous research has attempted to automatically generate them from source code. In this paper, we propose a new approach to invariant generation using search. We reuse the trace generation front-end of existing tool Daikon and integrate it with genetic programming and a mutation testing tool. We demonstrate that our system can find the same invariants through search that Daikon produces via template instantiation, and we also find useful invariants that Daikon does not. We then present a method of ranking invariants such that we can identify those that are most interesting, through a novel application of program mutation
The Synthesizability of Molecules Proposed by Generative Models
The discovery of functional molecules is an expensive and time-consuming
process, exemplified by the rising costs of small molecule therapeutic
discovery. One class of techniques of growing interest for early-stage drug
discovery is de novo molecular generation and optimization, catalyzed by the
development of new deep learning approaches. These techniques can suggest novel
molecular structures intended to maximize a multi-objective function, e.g.,
suitability as a therapeutic against a particular target, without relying on
brute-force exploration of a chemical space. However, the utility of these
approaches is stymied by ignorance of synthesizability. To highlight the
severity of this issue, we use a data-driven computer-aided synthesis planning
program to quantify how often molecules proposed by state-of-the-art generative
models cannot be readily synthesized. Our analysis demonstrates that there are
several tasks for which these models generate unrealistic molecular structures
despite performing well on popular quantitative benchmarks. Synthetic
complexity heuristics can successfully bias generation toward
synthetically-tractable chemical space, although doing so necessarily detracts
from the primary objective. This analysis suggests that to improve the utility
of these models in real discovery workflows, new algorithm development is
warranted
- β¦