# Pool-Based Genetic Programming Using Evospace, Local Search and Bloat Control

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## Abstract

**:**

## 1. Introduction

## 2. Background

#### 2.1. neat-GP

#### 2.2. Local Search in Genetic Programming

#### 2.3. Integration LS into neat-GP

#### 2.4. EvoSpace

## 3. Distributing neat-GP-LS into the EvoSpace Model

#### 3.1. The Intra-Species Distance and Re-Speciation

#### 3.2. Experiments and Results

## 4. Conclusions and Future Work

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Example of the shared structure ${S}_{i,j}$ between two trees ${T}_{i}$ and ${T}_{j}$ ([2], with permission from Springer).

**Figure 2.**Example of the tree transformation for the LS process ([2], with permission from Springer).

**Figure 3.**General flow diagram of the neat-GP-LS algorithm ([2], with permission from Springer).

**Figure 6.**Implementation of the neat-GP-LS algorithm in EvoSpace, where the samples taken by each EvoWorker correspond to a complete species.

**Figure 7.**Performance of a single run of the PEA implementation of neat-GP-LS in EvoSpace for: Housing (

**a**), (

**d**); Concrete (

**b**), (

**e**); and Energy Cooling (

**d**), (

**f**). The plots in the left column show the evolution of the training and testing RMSE. The plots in the right column show the evolution of the average program size. All plots are ordered based on the number of samples taken from the EvoStore.

**Figure 8.**Box plot comparison of the sequential and the EvoSpace implementation of the neat-GP-LS algorithm on the testing RMSE.

**Figure 9.**Box plot comparison of the sequential and the EvoSpace implementation of the neat-GP-LS algorithm on the average size of individuals given in number of nodes.

Problems | No. Instances | No. Features | Description |
---|---|---|---|

Housing [23] | 506 | 14 | Concerns housing values in suburbs of Boston. |

Concrete [24] | 1030 | 9 | The concrete compressive strength is a highly nonlinear function of age and ingredients. |

Energy Heating [25] | 768 | 9 | This study looked into assessing the heating load requirements of buildings as a function of building parameters. |

Energy Cooling [25] | 768 | 9 | This study looked into assessing the cooling load requirements of buildings as a function of building parameters. |

Tower [26] | 5000 | 26 | An industrial data set of a gas chromatography measurement of the composition of a distillation tower. |

Yacht [27] | 308 | 7 | Delft data set, used to predict the hydodynamic performance of sailing yachts from dimensions and velocity. |

Parameter | neat-GP-LS |
---|---|

Runs | 30 |

Population | 100 |

Generations | 10 |

Training set | 70% |

Testing set | 30% |

Operators Crossover (${p}_{c}$), Mutation (${p}_{m}$) | ${p}_{c}$=0.9, ${p}_{m}$=0.1 |

Tree initialization | Ramped Half-and-Half, maximum depth 6. |

Function set | +,-,x,sin,cos,log,sqrt,tan,tanh, constants |

Terminal set | Input variables and constants as indicated in each real-world problem. |

Selection for reproduction | Eliminate the ${p}_{worst}=50\%$ worst individuals of each species. |

Elitism | Do not penalize the best individual of each species. |

Species threshold value | $h=0.15$ with $\beta =0.5$ |

Local optimization probability | ${P}_{s}=0.5$ |

**Table 3.**Friedman test p-values, comparing the sequential neat-GP-LS and the EvoSpace implementation based on test RMSE and average size of the final population. Bold indicates that the null-hypothesis was rejected at the $\alpha =0.05$ significance level.

test | size | |

Problem | p-value | |

Housing | 0.2733 | 0.0114 |

Concrete | 0.0010 | 0.0010 |

Energy Cooling | 0.0578 | 0.0285 |

Energy Heating | 0.0114 | 1.000 |

Tower | 0.0285 | 0.0114 |

Yacht | 0.2059 | 0.0114 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Juárez-Smith, P.; Trujillo, L.; García-Valdez, M.; Fernández de Vega, F.; Chávez, F. Pool-Based Genetic Programming Using Evospace, Local Search and Bloat Control. *Math. Comput. Appl.* **2019**, *24*, 78.
https://doi.org/10.3390/mca24030078

**AMA Style**

Juárez-Smith P, Trujillo L, García-Valdez M, Fernández de Vega F, Chávez F. Pool-Based Genetic Programming Using Evospace, Local Search and Bloat Control. *Mathematical and Computational Applications*. 2019; 24(3):78.
https://doi.org/10.3390/mca24030078

**Chicago/Turabian Style**

Juárez-Smith, Perla, Leonardo Trujillo, Mario García-Valdez, Francisco Fernández de Vega, and Francisco Chávez. 2019. "Pool-Based Genetic Programming Using Evospace, Local Search and Bloat Control" *Mathematical and Computational Applications* 24, no. 3: 78.
https://doi.org/10.3390/mca24030078