scholarly journals Polymorphic symmetric multiple dispatch with variance

2019 ◽  
Vol 3 (POPL) ◽  
pp. 1-28 ◽  
Author(s):  
Gyunghee Park ◽  
Jaemin Hong ◽  
Guy L. Steele Jr. ◽  
Sukyoung Ryu
Keyword(s):  
Multiple Dispatch

scholarly journals Fresa: A Plant Propagation Algorithm for Black-Box Single and Multiple Objective Optimization

2021 ◽  
Vol 2 (4) ◽  
Keyword(s):  
Mathematical Programming ◽  
Objective Function ◽  
Optimization Problems ◽  
Population Based ◽  
Black Box ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Plant Propagation ◽  
Propagation Algorithm ◽  
Multiple Dispatch

Fresa implements a nature inspired plant propagation algorithm for the solution of single and multiple objective optimization problems. The method is population based and evolutionary. Treating the objective function as a black box, the implementation is able to solve problems exhibiting behaviour that is challenging for mathematical programming methods. Fresa is easily adapted to new problems which may benefit from bespoke representations of solutions by taking advantage of the dynamic typing and multiple dispatch capabilities of the Julia language. Further, the support for threads in Julia enables an efficient implementation on multi-core computers.

Achieving Multiple Dispatch in Hybrid Statically and Dynamically Typed Languages

2013 ◽  
pp. 703-713 ◽  
Author(s):  
Francisco Ortin ◽  
Miguel Garcia ◽  
Jose M. Redondo ◽  
Jose Quiroga
Keyword(s):  
Multiple Dispatch ◽  
Typed Languages

Modular multiple dispatch with multiple inheritance

2007 ◽  
Author(s):  
Eric Allen ◽  
J. J. Hallett ◽  
Victor Luchangco ◽  
Sukyoung Ryu ◽  
Guy L. Steele
Keyword(s):  
Multiple Inheritance ◽  
Multiple Dispatch

scholarly journals Multiple Dispatch in Practice

2021 ◽  
Author(s):  
◽  
Radu Muschevici
Keyword(s):  
Empirical Study ◽  
Programming Languages ◽  
Virtual Machine ◽  
Object Oriented ◽  
Run Time ◽  
Multiple Dispatch ◽  
Object Oriented Languages

<p>Multiple dispatch uses the run time types of more than one argument to a method call to determine which method body to run. While several languages over the last 20 years have provided multiple dispatch, most object-oriented languages still support only single dispatch - forcing programmers to implement multiple dispatch manually when required. This thesis presents an empirical study of the use of multiple dispatch in practice, considering six languages that support multiple dispatch. We hope that this study will help programmers understand the uses and abuses of multiple dispatch; virtual machine implementors optimise multiple dispatch; and language designers to evaluate the choice of providing multiple dispatch in new programming languages.</p>

scholarly journals Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems Using Multi-objective Reinforcement Learning

2021 ◽  
Vol 5 (4) ◽  
pp. 1-24
Author(s):  
Jianguo Chen ◽  
Kenli Li ◽  
Keqin Li ◽  
Philip S. Yu ◽  
Zeng Zeng
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Balance ◽  
Supply And Demand ◽  
Pareto Frontier ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Optimal Dispatch ◽  
Multi Objective ◽  
Dispatching Algorithm ◽  
Multiple Dispatch

As a new generation of Public Bicycle-sharing Systems (PBS), the Dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to use artificial intelligence to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for DL-PBS. In this article, we propose MORL-BD, a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning to provide the optimal bicycle dispatching solution for DL-PBS. We model the DL-PBS system from the perspective of cyber-physical systems and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching. We define the multi-route bicycle dispatching problem as a multi-objective optimization problem by considering the optimization objectives of dispatching costs, dispatch truck's initial load, workload balance among the trucks, and the dynamic balance of bicycle supply and demand. On this basis, the collaborative multi-route bicycle dispatching problem among multiple dispatch trucks is modeled as a multi-agent and multi-objective reinforcement learning model. All dispatch paths between parking spots are defined as state spaces, and the reciprocal of dispatching costs is defined as a reward. Each dispatch truck is equipped with an agent to learn the optimal dispatch path in the dynamic DL-PBS network. We create an elite list to store the Pareto optimal solutions of bicycle dispatch paths found in each action, and finally get the Pareto frontier. Experimental results on the actual DL-PBS show that compared with existing methods, MORL-BD can find a higher quality Pareto frontier with less execution time.

scholarly journals Type checking modular multiple dispatch with parametric polymorphism and multiple inheritance

2011 ◽  
Vol 46 (10) ◽  
pp. 973-992
Author(s):  
Eric Allen ◽  
Justin Hilburn ◽  
Scott Kilpatrick ◽  
Victor Luchangco ◽  
Sukyoung Ryu ◽  
...  
Keyword(s):  
Type Checking ◽  
Multiple Inheritance ◽  
Parametric Polymorphism ◽  
Multiple Dispatch
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