2.2.2 MEP method

The MEP uses linear chromosomes, making it an advanced and illustrative linear-based GP approach. The core MEP System is very close to the core GEP System. The ability to encode many pieces of software (alternatives) into a single chromosome is a defining feature of MEP, a relatively modern offshoot of the GP approach. The best chromosome is then chosen by measuring fitness [60], yielding the ultimate solution. The process of a twofold environment being recombined into dual unique descendants, according to Oltean and Grosan [59], leads to the assortment of two parents. As can be seen in Fig 4, the process keeps going till the optimal package is found prior to the termination criterion. This is the site where newborn mutations take place. The MEP approach permits the suitability of several factors, much like the GEP model. Multi-expression programming is governed by a number of criteria, such as the number and extent of subpopulations, the length of the algorithm/code, the probability of crossover, and the set of functions [67]. When the population extent is the total number of packages, assessing the population is more difficult, and taking the population size into account is more time-consuming. The size of the generated mathematical expressions is also significantly affected by the length of the code. The amount of MEP parameters used to create a reliable model of W.A is shown in Table 3.

The evaluation and modeling steps of both methods often make use of literature data sets [57]. Some researchers believe that the tremendously prevalent linear GP approaches, including the MEP and GEP approaches, can more reliably predict the qualities of sustainable concrete. Grosan and Abraham [68] found that when compared to other neural network-based approaches, the combination of linear genetic programming (LGP) with MEP yielded the best results. The method by which the GEP actually operates is somewhat more intricate than that of the MEP [67]. Despite the fact that MEP is less compact than GEP [58], it varies in that (i) code can be re-utilized in MEP, (ii) non-coding sections need not be displayed at a specific position within the chromosomes, and (iii) function argument pointers are plainly encoded in the MEP. The GEP is believed to have superior functionality because it possesses the "head" and "tail" of a distinctive GEP chromosome, mutually of which are loaded with ciphers that efficiently encrypt syntactically correct software programs [59]. This means that each of these genetic methods for engineering problems needs to be evaluated and assessed in greater depth.