@techreport(Langdon:1995:ppp,
  author = 	{W. B. Langdon},
  title =	{Pareto, Population Partitioning, Price and Genetic Programming},
  institution =	{University College London},
  year = 	1995,
  type =	{Research Note},
  number =	{RN/95/29},
  address =	{Gower Street, London WC1E 6BT, UK},
  month =	{April},
  note =	{Submitted to AAAI Fall 1995 Genetic Programming Symposium},
  keywords =    {Automatic Programming, Machine Learning, 
	Genetic Programming, Genetic Algorithms, Artificial Evolution,
	Pareto fitness, Demes},
  url =         {ftp://cs.ucl.ac.uk/genetic/papers/WBL_aaai-pppGP.ps},
  abstract =	{
A description of a use of Pareto optimality in genetic programming is
given and an analogy with Genetic Algorithm fitness niches is drawn.
Techniques to either spread the population across many pareto optimal
fitness values or to reduce the spread are described. It is speculated
that a wide spread may not aid Genetic Programming. It is suggested
that this might give useful insight into many GPs whose fitness is
composed of several sub-objectives.
 
The successful use of demic populations in GP leads to speculation
that smaller evolutionary steps might aid GP in the long run.
 
An example is given where Price's covariance theorem helped when
designing a GP fitness function.
},
  notes = 	{},
  size = 	{11 pages}
)