Efficient evolutionary optimization through the use of a cultural algorithm

Abstract

This paper introduces a cultural algorithm that uses domain knowledge to improve the performance of an evolutionary programming technique adopted for constrained optimization. The proposed approach extracts domain knowledge during the evolutionary process and builds a map of the feasible region to guide the search more efficiently. Additionally, in order to have a more efficient memory management scheme, the current implementation uses 2 ⁿ -trees to store this map of the feasible region. Results indicate that the approach is able to produce very competitive results with respect to other optimization techniques at a considerably lower computational cost.

Keywords:

• Cultural algorithms
• Evolutionary programming
• Constrained optimization

Acknowledgements

The first author acknowledges support from the Consejo Nacional de Ciencia y Tecnología (CONACyT) through project number 32999-A. The second author acknowledges support from CONACyT through a scholarship to pursue graduate studies at the Computer Science Section of the Electrical Engineering Department of CINVESTAV-IPN.

Notes

¹Other authors have also proposed the use of a map of the feasible region. See for example Ref. [23].

Figure 2 The figure at the top illustrates the feasible region of a problem. The figure at the bottom illustrates the representation of the constraints part of the belief space for the search space of the same problem. In this example, the intervals stored in the normative part must be [0.6, 2.6] for x ₁, and [3, 5] for x ₂.