Pipelined Genetic Algorithm for Optimization

This project involves implementing a pipelined genetic algorithm to optimize complex functions. Genetic algorithms (GAs) are search heuristics that mimic the process of natural selection to generate useful solutions for optimization and search problems.

Project Description

Overview of Genetic Algorithm

A genetic algorithm typically includes the following steps:

1. Initialization:

   • Generate an initial population randomly.
   • Each individual in the population represents a potential solution to the problem.

2. Selection:

   • Evaluate the fitness of each individual.
   • Select individuals based on their fitness to act as parents for the next generation.

3. Crossover:

   • Combine pairs of parents to produce offspring.
   • This process involves exchanging portions of their structures to create new individuals.

4. Mutation:

   • Introduce random changes to some individuals.
   • This helps maintain genetic diversity within the population.

5. Fitness Computation:

   • Evaluate the fitness of the offspring produced by crossover and mutation.

6. Replacement:

   • Replace the least fit individuals with the new offspring to form a new generation.
   • Repeat the process until a termination condition is met (e.g., a certain number of generations or a satisfactory fitness level).

Pipeline Design

The genetic algorithm is implemented using a pipelined architecture to improve efficiency. The pipeline consists of four main stages:

1. Selection:

   • The top 3 individuals with the highest fitness are selected for crossover and mutation.
   • The selection process uses a uniform random number generator for diversity.

2. Crossover & Mutation:

   • Selected parents undergo crossover to produce new offspring.
   • Mutation introduces changes to some offspring to explore the search space.

3. Fitness Computation:

   • Fitness of new offspring is computed.
   • Fitness function: Fitness = Y + Z + X
     • X: Result of AND operation between input values.
     • Y: Result of AND operation between specific bits.
     • Z: Result of OR operation between specific bits.

4. Replacement:

   • The new offspring replace the least fit individuals in the population.
   • The process continues until the termination condition is met.

Results

• The pipeline implementation processes 2500 individuals over 20 generations.
• The best fitness achieved in the final generation was 191119.

Conclusion

This project demonstrates the implementation of a pipelined genetic algorithm for optimization tasks. The pipelined architecture significantly improves the efficiency of the genetic algorithm, enabling it to handle larger populations and more generations effectively.