Evolutionary Programming
A computational method inspired by biological evolution that iteratively optimizes solutions through mutation and selection of program behaviors.
Evolutionary Programming (EP) is a computational paradigm developed by Lawrence J. Fogel in the 1960s as part of the broader field of evolutionary computation. Unlike its cousin genetic algorithms, EP emphasizes the behavioral linkage between parent and offspring solutions, focusing on the phenotypic expression rather than genetic representation.
The core mechanism of EP involves:
- Initialization of a population of candidate solutions
- Mutation of these solutions to create offspring
- Selection based on fitness function evaluation
- Iteration of this process until meeting termination criteria
EP differs from other evolutionary algorithms in several key ways:
- It traditionally uses only mutation (no crossover)
- It emphasizes behavioral change over structural modification
- It maintains a stronger parent-offspring relationship
The theoretical foundation of EP rests on the concept of intelligence as adaptive behavior. Fogel viewed intelligence as the capability of a system to modify its behavior to meet goals in light of environmental constraints. This connects EP to broader ideas in cybernetics about adaptation and learning systems.
Applications include:
- Prediction problems
- System identification
- Control systems optimization
- Pattern recognition
- Machine learning
EP's relationship to natural evolution is more metaphorical than literal, focusing on the principles of variation and selection rather than detailed biological mechanisms. This connects it to self-organization and emergence in complex systems.
Key contributions to the field include:
- Meta-evolutionary programming
- Self-adaptive mutation rates
- Hybrid approaches combining EP with other optimization methods
The field continues to evolve, with modern implementations incorporating ideas from neural networks and artificial life. EP represents a significant bridge between biological systems and artificial intelligence, demonstrating how natural principles can inform computational problem-solving.
Criticisms and limitations include:
- Computational intensity
- Difficulty in parameter tuning
- Lack of theoretical convergence guarantees in some cases
Despite these challenges, EP remains an important tool in the broader toolkit of evolutionary computation and continues to influence modern approaches to adaptive systems and optimization.
The philosophical implications of EP connect to questions about the nature of intelligence, adaptation, and the relationship between natural and artificial evolution, making it relevant to discussions in philosophy of computation and artificial life.