Multi-Objective Genetic Programming for Classification with Unbalanced Data

2009 ◽  
pp. 370-380 ◽  
Author(s):  
Urvesh Bhowan ◽  
Mengjie Zhang ◽  
Mark Johnston
Keyword(s):  
Genetic Programming ◽  
Unbalanced Data ◽  
Multi Objective

scholarly journals Multi‐objective Downscaling of Precipitation Time Series by Genetic Programming

2021 ◽  
Author(s):  
Tanja Zerenner ◽  
Victor Venema ◽  
Petra Friederichs ◽  
Clemens Simmer
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Precipitation Time Series ◽  
Multi Objective

Multi-objective Synthesis of Quantum Circuits Using Genetic Programming

2018 ◽  
pp. 220-227
Author(s):  
Moein Sarvaghad-Moghaddam ◽  
Philipp Niemann ◽  
Rolf Drechsler
Keyword(s):  
Genetic Programming ◽  
Quantum Circuits ◽  
Multi Objective

scholarly journals Genetic Programming with Delayed Routing for Multi-Objective Dynamic Flexible Job Shop Scheduling

2020 ◽  
Author(s):  
Binzi Xu ◽  
Yi Mei ◽  
Yan Wang ◽  
Zhicheng Ji ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Dynamic Environment ◽  
Accurate Information ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Conflicting Objectives

Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e. the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this paper, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and more accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multi-objective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.

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