peptides [Counts, et al., 2015; Zhou, et al., 2020].

duction

ear algorithms enjoy an excellent interpretation and explanation

y, but may not be sufficient to explain the interplay between

in protease cleavage data or posttranslational modification data.

the decision-tree algorithms and the random forest algorithms

sed to deliver intelligent rules, these algorithms may not deliver

ules because both types of algorithms derive rules based on the

principles. Discovering the best solution for a system among a

mber of candidates is prohibitive and problematic if the exhaustive

has to be used. Avoiding the expensive and exhaustive search

, the use of an optimisation process is considered as a reasonable

Inspired by the Darwin’s natural evolutionary theory, various

ary computation algorithms have been developed, aiming to

natural evolutionary process using a computer program in a

so as to examine how the rule of survival of the fittest can be

or the optimisation problems of rule discovery [Fogel, et al., 1966;

1975; Goldberg, 1989].

are mainly three types of evolutionary computation approaches.

the evolutionary algorithms (EA), the genetic algorithms (GA)

genetic programming algorithm (GP). The evolutionary

ms are normally used to optimise a numerical system with many

rs which are required for optimisation. For instance, the

ary algorithm can be used to optimise the parameters of a neural

This is because the conventional Newton’s method used to

the model parameters is very likely stuck in a local optimum and

tive function of a neural network model is complex [Angeline,

994; Brusic, et al., 1998; Bird, et al., 2019]. The use of the

ary algorithm can start with a pool of initial sets of weights. The

e Newton approach for each of an individual set of weights can

one of many local optimums including the one which is the

ptimum. Among many candidate solutions, the evolutionary