58,106 research outputs found
Evolutionary Algorithms
Evolutionary algorithms (EAs) are population-based metaheuristics, originally
inspired by aspects of natural evolution. Modern varieties incorporate a broad
mixture of search mechanisms, and tend to blend inspiration from nature with
pragmatic engineering concerns; however, all EAs essentially operate by
maintaining a population of potential solutions and in some way artificially
'evolving' that population over time. Particularly well-known categories of EAs
include genetic algorithms (GAs), Genetic Programming (GP), and Evolution
Strategies (ES). EAs have proven very successful in practical applications,
particularly those requiring solutions to combinatorial problems. EAs are
highly flexible and can be configured to address any optimization task, without
the requirements for reformulation and/or simplification that would be needed
for other techniques. However, this flexibility goes hand in hand with a cost:
the tailoring of an EA's configuration and parameters, so as to provide robust
performance for a given class of tasks, is often a complex and time-consuming
process. This tailoring process is one of the many ongoing research areas
associated with EAs.Comment: To appear in R. Marti, P. Pardalos, and M. Resende, eds., Handbook of
Heuristics, Springe
Genetic Programming for Multibiometrics
Biometric systems suffer from some drawbacks: a biometric system can provide
in general good performances except with some individuals as its performance
depends highly on the quality of the capture. One solution to solve some of
these problems is to use multibiometrics where different biometric systems are
combined together (multiple captures of the same biometric modality, multiple
feature extraction algorithms, multiple biometric modalities...). In this
paper, we are interested in score level fusion functions application (i.e., we
use a multibiometric authentication scheme which accept or deny the claimant
for using an application). In the state of the art, the weighted sum of scores
(which is a linear classifier) and the use of an SVM (which is a non linear
classifier) provided by different biometric systems provide one of the best
performances. We present a new method based on the use of genetic programming
giving similar or better performances (depending on the complexity of the
database). We derive a score fusion function by assembling some classical
primitives functions (+, *, -, ...). We have validated the proposed method on
three significant biometric benchmark datasets from the state of the art
Generalized Hybrid Evolutionary Algorithm Framework with a Mutation Operator Requiring no Adaptation
This paper presents a generalized hybrid evolutionary optimization structure that not only combines both nondeterministic and deterministic algorithms on their individual merits and distinct advantages, but also offers behaviors of the three originating classes of evolutionary algorithms (EAs). In addition, a robust mutation operator is developed in place of the necessity of mutation adaptation, based on the mutation properties of binary-coded individuals in a genetic algorithm. The behaviour of this mutation operator is examined in full and its performance is compared with adaptive mutations. The results show that the new mutation operator outperforms adaptive mutation operators while reducing complications of extra adaptive parameters in an EA representation
Evolutionary Computation in High Energy Physics
Evolutionary Computation is a branch of computer science with which,
traditionally, High Energy Physics has fewer connections. Its methods were
investigated in this field, mainly for data analysis tasks. These methods and
studies are, however, less known in the high energy physics community and this
motivated us to prepare this lecture. The lecture presents a general overview
of the main types of algorithms based on Evolutionary Computation, as well as a
review of their applications in High Energy Physics.Comment: Lecture presented at 2006 Inverted CERN School of Computing; to be
published in the school proceedings (CERN Yellow Report
GPTIPS 2: an open-source software platform for symbolic data mining
GPTIPS is a free, open source MATLAB based software platform for symbolic
data mining (SDM). It uses a multigene variant of the biologically inspired
machine learning method of genetic programming (MGGP) as the engine that drives
the automatic model discovery process. Symbolic data mining is the process of
extracting hidden, meaningful relationships from data in the form of symbolic
equations. In contrast to other data-mining methods, the structural
transparency of the generated predictive equations can give new insights into
the physical systems or processes that generated the data. Furthermore, this
transparency makes the models very easy to deploy outside of MATLAB. The
rationale behind GPTIPS is to reduce the technical barriers to using,
understanding, visualising and deploying GP based symbolic models of data,
whilst at the same time remaining highly customisable and delivering robust
numerical performance for power users. In this chapter, notable new features of
the latest version of the software are discussed with these aims in mind.
Additionally, a simplified variant of the MGGP high level gene crossover
mechanism is proposed. It is demonstrated that the new functionality of GPTIPS
2 (a) facilitates the discovery of compact symbolic relationships from data
using multiple approaches, e.g. using novel gene-centric visualisation analysis
to mitigate horizontal bloat and reduce complexity in multigene symbolic
regression models (b) provides numerous methods for visualising the properties
of symbolic models (c) emphasises the generation of graphically navigable
libraries of models that are optimal in terms of the Pareto trade off surface
of model performance and complexity and (d) expedites real world applications
by the simple, rapid and robust deployment of symbolic models outside the
software environment they were developed in.Comment: 26 pages, accepted for publication in the Springer Handbook of
Genetic Programming Applications (2015, in press
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