# Individuals and the population¶

## Representation¶

At the beginning of a EA, a collection of individuals are generated. This collection is called a population or generation. Typically, these individuals are created by randomly sampling parameters from a search space – though other methods exist. Each individual represents a solution to the problem at hand; in the case of genetic algorithms, this representation is a bit string or chromosome as it imitates an actual genetic chromosome.

The EDO method deals with the creation and adjustment of entire datasets. As such, there is no encoding to that aspect of an individual. In addition to this dataset, individuals are represented by a list of probability distributions. Each of these distributions acts a set of instructions on how to create, inherit from and mutate the values of the corresponding column in the dataset. These objects are stored as class attributes in the edo.individual.Individual class.

## Creation¶

The parameter space from which individual datasets are generated is defined by the row_limits, col_limits and families parameters in edo.DataOptimiser. The first two describe the dimensional limits of the dataset (i.e. how tall or wide it can be) while the latter is a pool of families of probability distributions with which to fill in the dataset.

Note

You can consider a family as a sort of factory that manages and distributes independent copies of a particular probability distribution.

The step of creating an individual is to sample a number of rows and columns, creating a “skeleton” to be filled. Then, according to any family-specific limits defined in col_limits, each column is filled with values by:

1. Sampling a family from the pool.
2. Creating a new (or using an existing) copy of that family’s distribution.
3. Creating an instance of that distribution and sampling values from it to fill the column.

A diagram depicting this process is given below: