**New Approaches**

**Chapter 0**

**Chapter 1**

**Using Quantitative Genetics and Phenotypic**

When evolving executable objects, the primary focus is on the behavioral repertoire that objects exhibit. For an evolutionary algorithm (EA) approach to be effective, a fitness function must be devised that provides differential feedback across evolving objects and provides some sort of fitness gradient to guide an EA in useful directions. It is fairly well understood that needle-in-a-haystack fitness landscapes should be avoided (e.g., was the tasked accomplished

One approach takes its cue from animal trainers who achieve complex behaviors via some sort of "shaping" methodology in which simpler behaviors are learned first, and then more complex behaviors are built up from these behavior "building blocks". Similar ideas and approaches show up in the educational literature in the form of "scaffolding" techniques. The main concern with such an approach in EC in general and GP in particular is the heavy

As a consequence most EA/GP approaches attempt to capture this kind of information in a single fitness function with the hope of providing the necessary bias to achieve the desired behavior without any explicit intervention along the way. One attempt to achieve this involves identifying important quantifiable behavior traits and including them in the EA/GP fitness function. If one then proceeds with a standard "blackbox" optimization approach in which behavioral fitness feedback is just a single scalar, there are in general a large number of genotypes (executable objects) that can produce identical fitness values and small changes in executable structures can lead to large changes in behavioral fitness. In general, what is

We believe that there are existing tools and techniques that have been developed in the field of quantitative genetics that can be used to get at this notion of behavioral inheritability. In this chapter we first give a basic tutorial on the quantitative genetics approach and metrics required to analyze evolutionary dynamics, as the first step in understanding how this can be used for GP analysis. We then discuss some higher level issues for obtaining useful

> ©2012 Kamath et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly

©2012 Kamath et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Traits in Genetic Programming**

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50143

**1. Introduction**

Uday Kamath, Jeffrey K. Bassett and Kenneth A. De Jong

or not), but much less well understood as to the alternatives.

dependence on a trainer within the evolutionary loop.

needed is a notion of behavioral inheritance.

cited.
