Yet Another GA Book

I recently came across this book: "Practical Genetic Algorithms". I already studied 3 chapters, and found it really intresting. As the name implies it has a practical approach in which the authors try to explain the implementation details of GA.

In most of the GA books I've ever read the discussion of GA operators such as crossover or mutation is vague. Statements like "Mutation is to change non-junk part of the chromosome with some probability" are everywhere, and there is usually no distinction between real-valued and binary GAs. Conversely this books explains all these different variations with reasonable detail, and has good references to relevant papers. For example the book clearly describes different types of selection methods, different variations of crossover and mutation in both real-valued, and binary GAs, and for all of these it has references to papers where a more in-depth discussion could be found.

In short this book is a pragmatic tutorial on GA which has a good balance between theory an practice.

I guess anybody who has every worked with a GA can borrow a lot of ideas from this book in order to fine tune their implementation.

Step by Step

Re-evaluation plan for Gyoomard is to assess the results of different stages step by step. This would mean to start off with no control and run the biped simulation as a Passive Dynamic Walker, then add a very simple control with a simple neural network. Finally achieving the full problem configuration.

Another main area for investigation the exact mechanism for the GA. It seems currently that the solver falls into local maximum points.

One other feature which can be added in the step by step progression is a support to help Gyoomard in the walking process. The support will be used for learning and then taken out at some future stage.

Coevolution and Distribution

The idea of coevolution in genetic algorithms is very interesting. It might not be directly usable in the first milestone for Gyoomard which is the development of a pattern generator for walking but the idea should be valuable in the next milestones.

Distributed GAs use time and space to evlove and can be highly beneficial for parallel programming.

The following paper has a good discussion on the above topic:

Distributed Coevlotionary Genetic Algorithms for Multi-Criteria and Multi-Constrain Optimisatioin, Phil Husbands