<p>Initial population plays an important role in heuristic algorithms such as GA as it help to decrease the time those algorithms need to achieve an acceptable result. Furthermore, it may influence the quality of the final answer given by evolutionary algorithms. (<a href="http://arxiv.org/pdf/1406.4518.pdf" rel="nofollow">http://arxiv.org/pdf/1406.4518.pdf</a>)</p>

<p>so as you know about Koza's different population methods, you must also remember that each algorithm that is used, isn't 100% random, nor can it be as an algorithm can be used. therefore you can predict what the next value will be. 
another method you could potentially use is something called Uniform initialisation (refer to the free pdf : "a field guide to genetic programming"). the idea of this is that initially, when a population is created, within a few generations, due to crossing over and selection, the entire syntax tree could be lost within a few generations. Langdon (2000) came up with the idea of a <em>ramped uniform distribution</em> which effectively allows the user to specify the range of sizes a possible tree can have, and if a permutation of the tree is generated in the search space that doesn't fulfil the range of sizes, then the tree is automatically discarded, regardless of its fitness evaluation value. from here, the ramped uniform distribution will create an equal amount of trees depending on the range that you have used - all of which are random, unique permutations of the functions and terminal values you are using.  (again, refer to "a field guide on genetic programming" for more detail)
this method can be quite useful in terms of sampling where the desired solutions are asymmetric rather than symmetric (which is what the ramped half and half deal with). </p>

<p>other recommeded readings for population initialisation:
<a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.962&amp;rep=rep1&amp;type=pdf" rel="nofollow">http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.962&amp;rep=rep1&amp;type=pdf</a></p>
Genetic Programming - algorithms for population initialization
<p>Can someone provide me some pointers on population initialization algorithms for genetic programming? </p>

<p>I already know about the Grown, Full, Ramped half-half (taken from "A Field Guide to Genetic Programming") and saw one new algorithm <a href="http://www.cs.gmu.edu/~sean/papers/treecreation.pdf" rel="nofollow">Two Fast Tree-Creation</a> (haven't read the paper yet.).</p>