object ve u ct o

cu ve s co p e

w t

o e t a

o e sadd e

e problem becomes complex. This is because it is impossible to

e that all the saddle points have the same minimum objective

value. In addition, the Newton’s method can only guarantee an

ion process to reach one saddle point based on one initial point

ective function curve. Therefore, one initial point leads to one of

saddle points using the Newton’s method. Whenever one saddle

been reached, the Newton’s optimisation process will never be

move out from the saddle point even if this saddle point does not

nd to the minimum objective function value.

fore, the evolutionary computation techniques or algorithms have

sidered for the optimisation problems where a problem requires

x objective function curve with multiple saddle points [Fogel, et

; Holland, 1975; Goldberg, 1989]. The basic principle of the

ary computation approaches is to start from multiple initial

an objective function curve. Multiple models are constructed

e Newton’s method or similar greatest descent methods

eously from these multiple initial points. In an evolutionary

ion model, a pool of candidates (or candidate solutions) is

Within these candidates, the principle of survival of the fittest is

Only those candidates with the best fitness measurements have

to maintain in the pool and have the right to breed new offspring,

e used to replace those which fail the designed fitness criterion.

this kind of evolution process, an optimal candidate or solution

em can be discovered finally. The evolutionary computation

es have been widely used in many biological/medical research

with success [Fogel, 2008; Francois and Siggia, 2008; Rostain, et

An, et al., 2020; Eremeev and Spirov, 2021].

chapters

k is composed of nine chapters. From Chapter 2 to Chapter 8,

apter is designed to introduce a specific biological pattern