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Genetic

Algorithm and

Genetic

Programming

Crossovers in Nature

• Two parental chromosomes exchange part of their genetic information to create new hybrid combinations (recombinant).
• No loss of genes, but an exchange of genes between two previous chromosomes.
• No new genes created, preexisting old ones mixed together.

Genetic

Algorithms

Genetic Algorithms

• 1. Randomly initialize a population of chromosomes.
• 2. While the terminating criteria have not been

satisfied:

• a. Evaluate the fitness of each chromosome:
  • i. Construct the phenotype (e.g. simulated robot) corresponding to the encoded genotype (chromosome).
  • ii. Evaluate the phenotype (e.g. measure the simulated robot’s walking abilities), in order to determine its fitness.
• b. Remove chromosomes with low fitness.
• c. Generate new chromosomes, using certain selection schemes and genetic operators.

Crossover and mutation.

Agenda

• What is Genetic Programming?
• Background/History.
• Why Genetic Programming?
• How Genetic Principles are Applied.
• Examples of Genetic Programs.
• Future of Genetic Programming.

Evolutionary

Strategies

Evolutionary Strategies

• Like GP no distinction between search and

solution space

• Individuals are represented as real-valued

vectors.

• Simple ES
  • one parent and one child
  • Child solution generated by randomly mutating the problem parameters of the parent.
• Susceptible to stagnation at local optima

Evolutionary

Programming

Evolutionary Programming

• Resembles ES, developed independently
• Early versions of EP applied to the evolution of transition table of finite state machines
• One population of solutions, reproduction is by mutation only
• Like ES operates on the decision variable of the problem directly (ie Genotype = Phenotype )
• Tournament selection of parents
  • better fitness more likely a parent
  • children generated until population doubled in size
  • everyone evaluated and the half of population with lowest fitness deleted.

Genetic

Programming

Genetic Programming

• John Koza, 1992
• Evolve program instead of bitstring
• Lisp program structure is best suited
  • Genetic operators can do simple replacements of sub-trees
  • All generated programs can be treated as legal (no syntax errors)

Please review about Behavioral systems, Genetic Algorithms and Genetic Programming from the book. Chapters 20, 21, 22, 23.

Background/History

• By John R. Koza, Stanford University.
• 1992, Genetic Programming Treatise -

“Genetic Programming. On the Programming of Computers by Means of Natural Selection.” - Origin of GP.

• Combining the idea of machine learning

and evolved tree structures.

Why Genetic Programming?

• It saves time by freeing the human from having to design complex algorithms.
• Not only designing the algorithms but creating ones that give optimal solutions.
• Again, Artificial Intelligence.