7,646 research outputs found
Balanced crossover operators in Genetic Algorithms
In several combinatorial optimization problems arising in cryptography and design theory, the admissible solutions must often satisfy a balancedness constraint, such as being represented by bitstrings with a fixed number of ones. For this reason, several works in the literature tackling these optimization problems with Genetic Algorithms (GA) introduced new balanced crossover operators which ensure that the offspring has the same balancedness characteristics of the parents. However, the use of such operators has never been thoroughly motivated, except for some generic considerations about search space reduction. In this paper, we undertake a rigorous statistical investigation on the effect of balanced and unbalanced crossover operators against three optimization problems from the area of cryptography and coding theory: nonlinear balanced Boolean functions, binary Orthogonal Arrays (OA) and bent functions. In particular, we consider three different balanced crossover operators (each with two variants: \u201cleft-to-right\u201d and \u201cshuffled\u201d), two of which have never been published before, and compare their performances with classic one-point crossover. We are able to confirm that the balanced crossover operators perform better than one-point crossover. Furthermore, in two out of three crossovers, the \u201cleft-to-right\u201d version performs better than the \u201cshuffled\u201d version
Exploring Task Mappings on Heterogeneous MPSoCs using a Bias-Elitist Genetic Algorithm
Exploration of task mappings plays a crucial role in achieving high
performance in heterogeneous multi-processor system-on-chip (MPSoC) platforms.
The problem of optimally mapping a set of tasks onto a set of given
heterogeneous processors for maximal throughput has been known, in general, to
be NP-complete. The problem is further exacerbated when multiple applications
(i.e., bigger task sets) and the communication between tasks are also
considered. Previous research has shown that Genetic Algorithms (GA) typically
are a good choice to solve this problem when the solution space is relatively
small. However, when the size of the problem space increases, classic genetic
algorithms still suffer from the problem of long evolution times. To address
this problem, this paper proposes a novel bias-elitist genetic algorithm that
is guided by domain-specific heuristics to speed up the evolution process.
Experimental results reveal that our proposed algorithm is able to handle large
scale task mapping problems and produces high-quality mapping solutions in only
a short time period.Comment: 9 pages, 11 figures, uses algorithm2e.st
Automating biomedical data science through tree-based pipeline optimization
Over the past decade, data science and machine learning has grown from a
mysterious art form to a staple tool across a variety of fields in academia,
business, and government. In this paper, we introduce the concept of tree-based
pipeline optimization for automating one of the most tedious parts of machine
learning---pipeline design. We implement a Tree-based Pipeline Optimization
Tool (TPOT) and demonstrate its effectiveness on a series of simulated and
real-world genetic data sets. In particular, we show that TPOT can build
machine learning pipelines that achieve competitive classification accuracy and
discover novel pipeline operators---such as synthetic feature
constructors---that significantly improve classification accuracy on these data
sets. We also highlight the current challenges to pipeline optimization, such
as the tendency to produce pipelines that overfit the data, and suggest future
research paths to overcome these challenges. As such, this work represents an
early step toward fully automating machine learning pipeline design.Comment: 16 pages, 5 figures, to appear in EvoBIO 2016 proceeding
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Evolving cellular automata to generate nonlinear sequences with desirable properties
This paper presents a new chromosomal representation and associated genetic operators for the evolution of highly nonlinear cellular automata that generate pseudorandom number sequences with desirable properties ensured. This chromosomal representation reduces the computational complexity of genetic operators to evolve valid solutions while facilitating fitness evaluation based on the DIEHARD statistical tests
Memetic Multilevel Hypergraph Partitioning
Hypergraph partitioning has a wide range of important applications such as
VLSI design or scientific computing. With focus on solution quality, we develop
the first multilevel memetic algorithm to tackle the problem. Key components of
our contribution are new effective multilevel recombination and mutation
operations that provide a large amount of diversity. We perform a wide range of
experiments on a benchmark set containing instances from application areas such
VLSI, SAT solving, social networks, and scientific computing. Compared to the
state-of-the-art hypergraph partitioning tools hMetis, PaToH, and KaHyPar, our
new algorithm computes the best result on almost all instances
Sparse experimental design : an effective an efficient way discovering better genetic algorithm structures
The focus of this paper is the demonstration that sparse experimental design is a useful strategy for developing Genetic Algorithms. It is increasingly apparent from a number of reports and papers within a variety of different problem domains that the 'best' structure for a GA may be dependent upon the application. The GA structure is defined as both the types of operators and the parameters settings used during operation. The differences observed may be linked to the nature of the problem, the type of fitness function, or the depth or breadth of the problem under investigation. This paper demonstrates that advanced experimental design may be adopted to increase the understanding of the relationships between the GA structure and the problem domain, facilitating the selection of improved structures with a minimum of effort
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