Neural Networks and Evolutionary
Computation
Introduction to Evolutionary strategies (ES),
Introduction to Evolutionary Algorithm (EA)
Guided by – Dr. PR.Buvaneswari
Team members
21BAC10028 ANJALI S KUMARI
21BAC10029 DIVYANSH PANDEY
21BAC10032 KRITIKA TRIPATHI
21BAC10033 LAXMI PARMAR
21BAC10034 RAKESH LODHI
Introduction Evolutionary Strategies (ES)
• Evolutionary Strategies (ES) is a class of optimization algorithms that use
principles of natural selection and evolution to find optimal solutions to
problems. In ES, a population of candidate solutions is created, and each
solution is evaluated based on a fitness function that measures its quality.
The best solutions are then combined to create new solutions, which are
also evaluated and may replace the less fit ones. This process is repeated
over several generations until an acceptable solution is found or a stopping
criteria is met. ES is mainly used for problems where the objective function
is noisy or expensive to evaluate, and gradient-based methods are not
feasible.
Introduction to Evolutionary Algorithm
• An Evolutionary Algorithm (EA) is a family of optimization algorithms that
are inspired by the principles of biological evolution, such as selection,
mutation, and recombination. EAs are used to find solutions to problems in
optimization and search, where the objective is to find the best solution
among a large or even infinite set of possibilities.
Basic idea
• The basic idea behind an EA is to start with a population of candidate
solutions, evaluate each solution according to a predefined fitness function,
and use the results of the evaluation to guide the evolution of the population
towards better solutions. This is usually achieved by applying genetic
operators, such as selection, crossover, and mutation, to generate new
generations of solutions. The process is repeated until a satisfactory solution
is found or a stopping criterion is met.
used
• EAs are commonly used in a
variety of fields, including
machine learning, engineering,
and computer science, due to
their ability to search large and
complex solution spaces, even
when the objective function is
unknown or difficult to optimize
directly.
type of Evolutionary Algorithm (EA) with
example
• There are several types of Evolutionary Algorithms (EAs), including:
• Genetic Algorithms
• Evolutionary Strategies
• Evolutionary Programming (EP)
• Genetic Programming (GP)
Genetic Algorithms
.Genetic Algorithms: It is a search-based optimization technique based on the principles of Genetics
and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult
problems which otherwise would take a lifetime to solve.
This notion can be applied for a search problem. We consider a set of solutions for a problem and select
the set of best ones out of them.​
• Five phases are considered in a genetic algorithm.​
• Initial population​
• Fitness function​
• Selection​
• Crossover​
• Mutation​
Evolutionary Strategies
• Evolutionary Strategies: A family of Evolutionary Algorithms that use mutation and recombination as their
main search operators.
• Here's a simple example of how Evolutionary Strategies work:
• Start by generating a population of candidate solutions, each represented by a vector of real-valued parameters.
• Evaluate each candidate solution based on a fitness function that measures how well the parameters solve the
problem.
• Compute the mean and covariance matrix of the population based on the best candidate solutions.
• Generate a new population of candidate solutions by sampling from a multivariate normal distribution with the
mean and covariance matrix computed in the previous step.
• Repeat the above steps for a set number of generations or until a stopping criterion is met.
• The final solution is the best candidate solution found in the final generation. This solution is the set of
parameters that best solves the problem based on the fitness function.
•
Genetic Programming:
• Problem: Given a set of data points, find a mathematical function that best fits the data.
• Initialization: Generate a set of random mathematical functions (candidate solutions).
• Fitness evaluation: Calculate the error between the predicted output of each function and
the actual data points. The function with the lowest error is considered the fittest.
• Reproduction: Use genetic operations such as crossover and mutation to generate a new set
of functions based on the fittest individuals from the previous generation.
• Repeat steps 2 and 3 until a satisfactory solution is found or a termination criterion is met.
• Output: The final solution is the function that best fits the data.
• This example demonstrates how GP can be used to find the optimal solution to a problem
by iteratively generating, evaluating, and refining candidate solutions.
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