this paper aims to investigate the integrated production/distribution and inventory planning for perishable products with fixed life time in the constant condition of storage throughout a two-echelon supply chain by integrating producers and distributors. this problem arises from real environment in which multi-plant with multi-function lines produce multi-perishable products with fixed life ti...

This paper proposes a decision support system for tactical air combat environment using a combination of unsupervised learning for clustering the data and an ensemble of three well-known genetic programming techniques to classify the different decision regions accurately. The genetic programming techniques used are: Linear Genetic programming (LGP), Multi-Expression Programming (MEP) and Gene E...

This paper suggests a decision support system for tactical air combat environment using a combination of unsupervised learning for clustering the data and three well known genetic programming techniques to classify the different decision regions accurately. The genetic programming techniques used are: Linear Genetic programming (LGP), Multi Expression Programming (MEP) and Gene Expression Progr...

Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evo...

Semantics has become a key topic of research in Genetic Programming (GP). refers to the outputs (behaviour) GP individual when this is run on dataset. The majority works that focus semantic diversity single-objective indicates it highly beneficial evolutionary search. Surprisingly, there minuscule conducted semantics Multi-objective (MOGP). In work we make leap beyond our understanding MOGP and...

A comparison between four evolutionary techniques for solving symbolic regression problems is presented in this paper. The compared methods are multi-expression programming, gene expression programming, grammatical evolution, and linear genetic programming. The comparison includes all aspects of the considered evolutionary algorithms: individual representation, fitness assignment, genetic opera...

In this paper, we exploit the flexibility of multi-objective fitness functions, and the efficiency of the model structure selection ability of a standard genetic programming (GP) with the parameter estimation power of classical regression via multi-gene genetic programming (MGGP), to propose a new fusion technique for image quality assessment (IQA) that is called Multi-measures Fusion based on ...

In this work we design a genetic representation and its genetic operators to encode individuals for evolving Dynamic System Models in a Qualitative Differential Equation form, for System Identification. The representation proposed, can be implemented in almost every programming language without the need of complex data structures, this representation gives us the possibility to encode an indivi...