scholarly journals Generating and Optimizing Human-Readable Quantitative Program Trading Strategies through a Genetic Programming Framework

2021 ◽  
Vol 187 ◽  
pp. 613-617
Author(s):  
Bin Teng ◽  
Yufeng Shi ◽  
Xin Wang ◽  
Yunchuan Sun
Keyword(s):  
Genetic Programming ◽  
Trading Strategies ◽  
Programming Framework

A GRAMMAR-GUIDED GENETIC PROGRAMMING FRAMEWORK CONFIGURED FOR DATA MINING AND SOFTWARE TESTING

2006 ◽  
Vol 16 (02) ◽  
pp. 245-267 ◽  
Author(s):  
SILVIA REGINA VERGILIO ◽  
AURORA POZO
Keyword(s):  
Data Mining ◽  
Genetic Programming ◽  
Software Testing ◽  
Relational Databases ◽  
Programming Framework ◽  
Induction Technique

Genetic Programming (GP) is a powerful software induction technique that can be applied to solve a wide variety of problems. However, most researchers develop tailor-made GP tools for solving specific problems. These tools generally require significant modifications in their kernel to be adapted to other domains. In this paper, we explore the Grammar-Guided Genetic Programming (GGGP) approach as an alternative to overcome such limitation. We describe a GGGP based framework, named Chameleon, that can be easily configured to solve different problems. We explore the use of Chameleon in two domains, not usually addressed by works in the literature: in the task of mining relational databases and in the software testing activity. The presented results point out that the use of the grammar-guided approach helps us to obtain more generic GP frameworks and that they can contribute in the explored domains.

Living Melodies: Coevolution of Sonic Communication

Leonardo ◽  
2001 ◽  
Vol 34 (3) ◽  
pp. 243-248 ◽  
Author(s):  
Palle Dahlstedt ◽  
Mats G. Nordahl
Keyword(s):  
Genetic Programming ◽  
Foraging Behavior ◽  
Raw Material ◽  
Programming Framework ◽  
Entire World ◽  
Artificial World

The authors have constructed an artificial world of coevolving communicating agents. The behavior of the agents is described in terms of a simple genetic programming framework, which allows the evolution of foraging behavior and movement in order to reproduce, as well as sonic communication. The sound of the entire world is used as musical raw material for the work. Musically interesting and useful structures are found to emerge.

scholarly journals GSGP-C++ 2.0: A geometric semantic genetic programming framework

SoftwareX ◽  
2019 ◽  
Vol 10 ◽  
pp. 100313 ◽  
Author(s):  
Mauro Castelli ◽  
Luca Manzoni
Keyword(s):  
Genetic Programming ◽  
Programming Framework

A new genetic programming framework based on reaction systems

2013 ◽  
Vol 14 (4) ◽  
pp. 457-471
Author(s):  
Luca Manzoni ◽  
Mauro Castelli ◽  
Leonardo Vanneschi
Keyword(s):  
Genetic Programming ◽  
Reaction Systems ◽  
Programming Framework

A genetic programming framework for content-based image retrieval

2009 ◽  
Vol 42 (2) ◽  
pp. 283-292 ◽  
Author(s):  
Ricardo da S. Torres ◽  
Alexandre X. Falcão ◽  
Marcos A. Gonçalves ◽  
João P. Papa ◽  
Baoping Zhang ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Programming Framework

Social Simulation with Both Human Agents and Software Agents

2010 ◽  
pp. 95-117
Author(s):  
Shu-Heng Chen ◽  
Chung-Ching Tai ◽  
Tzai-Der Wang ◽  
Shu G. Wang
Keyword(s):  
Genetic Programming ◽  
Social Simulation ◽  
Trading Strategies ◽  
Agent Based ◽  
Learning Agents ◽  
Learning Capacity ◽  
Intellectual Abilities ◽  
Learning Capabilities ◽  
Human Experiments ◽  
Multi Agent

In this chapter, we will present agent-based simulations as well as human experiments in double auction markets. Our idea is to investigate the learning capabilities of human traders by studying learning agents constructed by Genetic Programming (GP), and the latter can further serve as a design platform in conducting human experiments. By manipulating the population size of GP traders, we attempt to characterize the innate heterogeneity in human being’s intellectual abilities. We find that GP traders are efficient in the sense that they can beat other trading strategies even with very limited learning capacity. A series of human experiments and multi-agent simulations are conducted and compared for an examination at the end of this chapter.

Social Simulation with Both Human Agents and Software Agents

2011 ◽  
pp. 867-888
Author(s):  
Shu-Heng Chen ◽  
Chung-Ching Tai ◽  
Tzai-Der Wang ◽  
Shu G. Wang
Keyword(s):  
Genetic Programming ◽  
Social Simulation ◽  
Trading Strategies ◽  
Agent Based ◽  
Learning Agents ◽  
Learning Capacity ◽  
Intellectual Abilities ◽  
Learning Capabilities ◽  
Human Experiments ◽  
Multi Agent

In this chapter, we will present agent-based simulations as well as human experiments in double auction markets. Our idea is to investigate the learning capabilities of human traders by studying learning agents constructed by Genetic Programming (GP), and the latter can further serve as a design platform in conducting human experiments. By manipulating the population size of GP traders, we attempt to characterize the innate heterogeneity in human being’s intellectual abilities. We find that GP traders are efficient in the sense that they can beat other trading strategies even with very limited learning capacity. A series of human experiments and multi-agent simulations are conducted and compared for an examination at the end of this chapter.

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