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List the problems that can be efficiently solved by Evolutionary Programming?
List the problems that can be efficiently solved by Evolutionary Programming?
Solution
Evolutionary Programming is a Global Optimization algorithm and is an instance of an
Evolutionary Algorithm from the field of Evolutionary Computation. The approach is a sibling
of other Evolutionary Algorithms such as the Genetic Algorithm, and Learning Classifier
Systems. It is sometimes confused with Genetic Programming given the similarity in name, and
more recently it shows a strong functional similarity to Evolution Strategies.
Inspiration
Evolutionary Programming is inspired by the theory of evolution by means of natural selection.
Specifically, the technique is inspired by macro-level or the species-level process of evolution
(phenotype, hereditary, variation) and is not concerned with the genetic mechanisms of evolution
(genome, chromosomes, genes, alleles).
Metaphor
A population of a species reproduce, creating progeny with small phenotypical variation. The
progeny and the parents compete based on their suitability to the environment, where the
generally more fit members constitute the subsequent generation and are provided with the
opportunity to reproduce themselves. This process repeats, improving the adaptive fit between
the species and the environment.
Strategy
The objective of the Evolutionary Programming algorithm is to maximize the suitability of a
collection of candidate solutions in the context of an objective function from the domain. This
objective is pursued by using an adaptive model with surrogates for the processes of evolution,
specifically hereditary (reproduction with variation) under competition. The representation used
for candidate solutions is directly assessable by a cost or objective function from the domain.
Problems for evolutionary algorithms
Evolutionary programming are useful for problems for which no mechanistic method is available
– or when unreasonable assumptions need to be made. Problems for which you cannot calculate
or derive a solution. Problems for which you have to find a solution.
Aspects of problems that lend them to Evolutionary Programming solution include:
Assignment of individuals into groups, wherever simple ranking and truncation will not work.
This usually involves interactions, whereby whether an individual should be in a group depends
on what other individuals will be in that group. Example: developing multiplex groupings for
genotyping.
Problems where thresholds are involved, for example when the value of a solution depends on
whether certain thresholds have been passed, or the fate of an individual depends on passing one
or more thresholds. Example: Supply chain optimizing to target multiple product end-points
and/or turnoff dates.
Combinatorially tedious problems, where many combinations of components can exist. Example:
The setting up of animal matings. Below are the few applications:
Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that
combine a discovery component (e.g. typically a genetic algorithm) with a learning component
(performing either supervised learning, reinforcement learning, or unsupervised learning).
Learning classifier systems seek to identify a set of context-dependent rules that collectively
store and apply knowledge in a piecewise manner in order to make predictions
Genetic programming (GP) is a technique whereby computer programs are encoded as a set of
genes that are then modified (evolved) using an evolutionary algorithm (often a genetic
algorithm, "GA") – it is an application of (for example) genetic algorithms where the space of
solutions consists of computer programs. The results are computer programs able to perform well
in a predefined task. The methods used to encode a computer program in an artificial
chromosome and to evaluate its fitness with respect to the predefined task are central in the GP
technique and still the subject of active research.
Gene expression programming (GEP) is an evolutionary algorithm that creates computer
programs or models. These computer programs are complex tree structures that learn and adapt
by changing their sizes, shapes, and composition, much like a living organism. And like living
organisms, the computer programs of GEP are also encoded in simple linear chromosomes of
fixed length. Thus, GEP is a genotype-phenotype system, benefiting from a simple genome to
keep and transmit the genetic information and a complex phenotype to explore the environment
and adapt to it.
Natural evolution strategies (NES) are a family of numerical optimization algorithms for black-
box problems. Similar in spirit to evolution strategies, they iteratively update the (continuous)
parameters of a search distribution by following the natural gradient towards higher expected
fitness.

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List the problems that can be efficiently solved by Evolutionary P.pdf

  • 1. List the problems that can be efficiently solved by Evolutionary Programming? List the problems that can be efficiently solved by Evolutionary Programming? Solution Evolutionary Programming is a Global Optimization algorithm and is an instance of an Evolutionary Algorithm from the field of Evolutionary Computation. The approach is a sibling of other Evolutionary Algorithms such as the Genetic Algorithm, and Learning Classifier Systems. It is sometimes confused with Genetic Programming given the similarity in name, and more recently it shows a strong functional similarity to Evolution Strategies. Inspiration Evolutionary Programming is inspired by the theory of evolution by means of natural selection. Specifically, the technique is inspired by macro-level or the species-level process of evolution (phenotype, hereditary, variation) and is not concerned with the genetic mechanisms of evolution (genome, chromosomes, genes, alleles). Metaphor A population of a species reproduce, creating progeny with small phenotypical variation. The progeny and the parents compete based on their suitability to the environment, where the generally more fit members constitute the subsequent generation and are provided with the opportunity to reproduce themselves. This process repeats, improving the adaptive fit between the species and the environment. Strategy The objective of the Evolutionary Programming algorithm is to maximize the suitability of a collection of candidate solutions in the context of an objective function from the domain. This objective is pursued by using an adaptive model with surrogates for the processes of evolution, specifically hereditary (reproduction with variation) under competition. The representation used for candidate solutions is directly assessable by a cost or objective function from the domain. Problems for evolutionary algorithms Evolutionary programming are useful for problems for which no mechanistic method is available – or when unreasonable assumptions need to be made. Problems for which you cannot calculate or derive a solution. Problems for which you have to find a solution. Aspects of problems that lend them to Evolutionary Programming solution include: Assignment of individuals into groups, wherever simple ranking and truncation will not work. This usually involves interactions, whereby whether an individual should be in a group depends
  • 2. on what other individuals will be in that group. Example: developing multiplex groupings for genotyping. Problems where thresholds are involved, for example when the value of a solution depends on whether certain thresholds have been passed, or the fate of an individual depends on passing one or more thresholds. Example: Supply chain optimizing to target multiple product end-points and/or turnoff dates. Combinatorially tedious problems, where many combinations of components can exist. Example: The setting up of animal matings. Below are the few applications: Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions Genetic programming (GP) is a technique whereby computer programs are encoded as a set of genes that are then modified (evolved) using an evolutionary algorithm (often a genetic algorithm, "GA") – it is an application of (for example) genetic algorithms where the space of solutions consists of computer programs. The results are computer programs able to perform well in a predefined task. The methods used to encode a computer program in an artificial chromosome and to evaluate its fitness with respect to the predefined task are central in the GP technique and still the subject of active research. Gene expression programming (GEP) is an evolutionary algorithm that creates computer programs or models. These computer programs are complex tree structures that learn and adapt by changing their sizes, shapes, and composition, much like a living organism. And like living organisms, the computer programs of GEP are also encoded in simple linear chromosomes of fixed length. Thus, GEP is a genotype-phenotype system, benefiting from a simple genome to keep and transmit the genetic information and a complex phenotype to explore the environment and adapt to it. Natural evolution strategies (NES) are a family of numerical optimization algorithms for black- box problems. Similar in spirit to evolution strategies, they iteratively update the (continuous) parameters of a search distribution by following the natural gradient towards higher expected fitness.