GA is a class of EA

A nice starting point is in Freitas (2002) book. There one finds a complete introduction on this matter: Alex A. Freitas. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2002. ISBN 3540433317.

I would rather suggest you to see these two books as an initializer, http://tocs.ulb.tu-darmstadt.de/28323289.pdf and http://dl.acm.org/citation.cfm?id=534133. These books are fairly simple and more generalized. You may understand the mechanism of the algorithm and then design a GA to solve your problem, like scheduling, TSP, data mining and so on. 

Genetic algorithms belong to the larger class of evolutionary algorithms (EA). EAs use mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection.

There are several evolutionary algorithms among them genetic algorithm

There are other algorithms similar to GA which is GA mixed with NN.  see this link in wikki http://en.wikipedia.org/wiki/Evolutionary_algorithm

Evolutionary Algorithms is a subfield of Computational Intelligence. Their algorithms use evolutionary mechanisms such as reproduction, mutation and selection, in order to test and evolve candidate solutions and return the best solution possible of a given problem.

Genetic Algorithms is just one of many approaches of this subfield. You can find a very good chapter about this subject in the following book (which is, in my opinion, ont of the best introductory books about Computational Intelligence that I have ever read): http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470035617.html

GA is a sub-class of EAs. Basically, there are 3 implementation of EAs: GAs, evolution strategies (ESs), and evolutionary programming (EP). Among these, GAs have proved to be the most popular of the 3 EAs. These algorithms are similar in general, yet there are big differences among them:

GAs: They were developed by Holland and thoroughly reviewed by Goldberg;

EAs: They were developed by Rechenberg and Schwefe; and

EP: It was developed by L.J. Fogel and D.B. Fogel.

Similarity and Differences:

All 3 operate on fixed length strings, which contain real values in ESs and EP and binary numbers in the canonical GA.

All 3 incorporate a mutation operator: for ESs and EP mutation is the driving force. GAs and ESs also use a recombination operator, which is the primary operator for the GA.

All 3 use a selection operator which applies evolutionary pressure, either instinctive (in ESs and EP, the operator determines which individuals will be excluded from the new population) or preservative (in the GA the operator selects individuals for breeding)..

In GAs and EP selection is probabilistic, while ESs use a deterministic selection. ESs and meta-EP allow self-adaptation, where parameters controlling mutation are allowed to evolve along with object variables. Finally, it is worth noting that the implementer is free to modify these algorithms. For example, the GA can be run using an integer alphabet.

Genetic Algorithm can be treated as a sub-field of Evolutionary Algorithm.Both of them belongs to the area of artificial intelligence.Apart from Genetic Algorithm there are other fields included as a part of Evolutionary Algorithm.

dear friend

To add to the above answers:

EA is a method and GA is a model.

You just see my publications for more clarification:

Mandal, I., Sairam, N. New machine-learning algorithms for prediction of Parkinson's disease (2014) International Journal of Systems Science, 45 (3), pp. 647-666. DOI: 10.1080/00207721.2012.724114

Mandal, I., Sairam, N. Accurate telemonitoring of Parkinson's disease diagnosis using robust inference system (2013) International Journal of Medical Informatics, 82 (5), pp. 359-377. DOI: 10.1016/j.ijmedinf.2012.10.006

Mandal, I., Sairam, N. Accurate prediction of coronary artery disease using reliable diagnosis system (2012) Journal of Medical Systems, 36 (5), pp. 3353-3373. DOI: 10.1007/s10916-012-9828-0

Mandal, I., Sairam, N. Enhanced classification performance using computational intelligence (2011) Communications in Computer and Information Science, 204 CCIS, pp. 384-391. DOI: 10.1007/978-3-642-24043-0_39

Mandal, I. Software reliability assessment using artificial neural network (2010) ICWET 2010 - International Conference and Workshop on Emerging Trends in Technology 2010, Conference Proceedings, pp. 698-699. DOI: 10.1145/1741906.1742067

Mandal, I. A low-power content-addressable memory (CAM) using pipelined search scheme (2010) ICWET 2010 - International Conference and Workshop on Emerging Trends in Technology 2010, Conference Proceedings, pp. 853-858. DOI: 10.1145/1741906.1742103

Mandal, I. A novel approach for accurate identification of splice junctions based on hybrid algorithms (2014) Journal of Biomolecular Structure and Dynamics

pp. 1-10 | DOI: 10.1080/07391102.2014.944218 PMID: 25203504 http://www.tandfonline.com/eprint/TzMeXxpEXxujtEATHwqY/full

I hope this helps you.

Best regards

Dr.Indrajit Mandal

Dear All,

Please fight the unwarranted downvotes of threads by reporting it to RG admin. As you see all the relavant responses (in page 1) have been downvoted!

I have been downvoted in ANN and GA thread and I have been passive!. I just deleted my downvoted response or unfollowed the threads in which I did not have a response. Perhaps it is time to take more approperiate actions such as:

• reporting it to RG admin
• Flag the response
• upvoting the good responses which are downvoted

It is not take much detective work to see who down voted previous responses. I think that researcher/scientist should either reconsider/undo your irrational downvotes or explain the reasoning!. We appreciate it.

RG admins, could you please investigate the matter. Mass/serial downvoting of good answers should not be allowed in a scientific forum like RG.

Please see  the other thread with the same downvoting pattern as well. both happened at the same time! 

it is happened in other Questions of GA and ANN threads 2 hour ago and it seems that the same downvoting patterns has been happening it for months.

https://www.researchgate.net/post/May_you_please_suggest_any_article_or_any_other_source_about_ANN_in_forecasting#543f33c7d11b8b07718b46c3

Respected sir, 

Same thing happened with me.

But if you'll look into your both questions you'll find the clue that who devoted the answers. (I am not blaming anyone, I am just giving my opinion) Using this strategy I found and messaged the person who devoted my answers and asked him for the reason (Perhaps, he was the one who devoted as he didn't responded to any of my messages). So I sent this issue with screen print of my answer to the admin without specifying anyone's name.

If possible use the same strategy or report the issue to admin directly.

Hopefully admin will listen to us and he will make some changes for devoting answers like with reasons or else.

Thank you for raising this topic.

An EA in general evolves anything (bit string, vectors, programs, ...)

A GA specifically evolves bit strings. 

Also see this paper for a more technical discussion

Authors,Title,Publication,Volume,Number,Pages,Year,Publisher

"Woodward, John R; ",GA or GP? that is not the question,"Evolutionary Computation, 2003. CEC'03. The 2003 Congress on",2,,1056-1063,2003,IEEE

Evolutionary algorithms use only mutation as the reproduction strategy while genetic algorithms use both crossover and mutation for reproduction. Its as simple as that and is found in most of the evolutionary systems reported in the literature.

GA is a sub-class of EA

Adding to former statements EA´s are a subclass of heuristics