(This is the final part in the Modern Genetic Algorithms Explained series, click here to go to the first post, or browse through all the parts with the Table Of Contents at the end of this post.)
In the past series, we have looked at genetic algorithms, simulated annealing, ant colony simulation and tabu search. Each method had its pro’s and cons, and there certainly is room for exploration left.
I hope you’ve enjoyed this series, or that it has at least sparked your interest in the topic.
Below are a few more (general resources), if you want to know more.
Other related techniques:
- Evolutionary programming
- Genetic programming
- Harmony search - this is interesting: it’s based on how musicians compose music, also see this website
- Memetic algorithms - a very new and interesting area in evolutionary optimization. It combines a genetic algorithm to the ability of learning (hence the name: “memes”)
- And many more…
- Introduction to Evolutionary Computing
- Evolutionary Computation
- How to Solve It: Modern Heuristics
- A Field Guide to Genetic Programming (the PDF is free!)
- Genetic Programming: On the Programming of Computers by Means of Natural Selection
A specialized event was held in the USA: Foundations Of Genetic Algorithms. You can find a lot of interesting information on their website.
Finally, I want to mention a software project which looks promising: Paradiseo:
A white-box object-oriented framework, portable (Windows, Unix and MacOS), dedicated to the flexible design of metaheuristics: solution-based metaheuristics (Local search, Simulated annealing, Iterated local search, Tabu search, …) and population-based metaheuristics (Genetic algorithm, Particle swarm optimization, Evolution strategy, Differential evolution algorithms, …).
Table Of Contents (click a link to jump to that post)
1- Introduction 2- Genetic Algorithms 3- CHC Eshelman 4- Simulated Annealing 5- Ant Colony Optimization 6- Tabu Search 7- Conclusion