Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design

Hodan, David; Mrázek, Vojtěch; Vašíček, Zdeněk

doi:10.1007/s10710-021-09416-6

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…Commonly used multiple-output functions for the evaluation and comparison of graph-based GP models and corresponding operators are arithmetic functions such as the digital adder and multiplier as well as combinational functions [1,8,12,29]. Since a large part of Boolean functions can be implemented as digital circuits a real-world application of graph-based GP is located in the field of evolvable hardware [28].…”

Section: Learning Of Boolean Functions With Known Input-output Mappingmentioning

confidence: 99%

Towards a General Boolean Function Benchmark Suite

Kalkreuth¹,

Vašíček

Husa

et al. 2023

Proceedings of the Companion Conference on Genetic and Evolutionary Computation

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over a decade ago, the first comprehensive review on the state of benchmarking in Genetic Programming (GP) analyzed the mismatch between the problems that are used to test the performance of GP systems and real-world problems. Since then, several benchmark suites in major GP problem domains have been proposed over time, which were able to fill some of the major gaps. In the framework of the first review about the state of benchmarking in GP, logic synthesis was classified as one of the major GP problem domains. However, a diverse and accessible benchmark suite for logic synthesis is still missing in the field of GP. In this work, we take a first step towards a benchmark suite for logic synthesis that covers different types of Boolean functions that are commonly used for the evaluation of GP systems. We also present baseline results that have been obtained by former work and in our evaluation experiments by using Cartesian Genetic Programming.

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Section: Learning Of Boolean Functions With Known Input-output Mappingmentioning

confidence: 99%

Towards a General Boolean Function Benchmark Suite

Kalkreuth¹,

Vašíček

Husa

et al. 2023

Proceedings of the Companion Conference on Genetic and Evolutionary Computation

Self Cite

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“…In general, the evolutionary design of circuits refers to the use of a randomly generated initial population, exploring possible solutions given a required behavior in a certain problem. The optimization refers to the minimization/maximization of some desired parameter, such as the number of logic gates, frequently motivated by practical needs [Hodan et al 2021]. However, finding a fully working solution is a hard task, and there are scalability issues in which the computational complexity grows exponentially with the number of inputs.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Clonal Selection Principle in Cartesian Genetic Programming for Designing Combinational Logic Circuits

Silva¹,

Prachedes²,

Bernardino³

et al. 2022

Anais Do XIX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2022)

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Evolutionary techniques have been used in the design and optimization of combinational logic circuits. This procedure is called evolvable hardware and Cartesian Genetic Programming (CGP) is the evolutionary technique with the best performance in this context. Despite the good results obtained by CGP techniques, its search procedure usually evolves a single candidate solution by an evolution strategy and this approach tends to be trapped in local optima. On the other hand, clonal selection techniques in general, and CLONALG in particular, were designed to avoid converging to a low-quality local optimum. Thus, we propose here using the representation of CGP with the search procedure of a Clonal Selection Algorithm to minimize the number of transistors of combinational logic circuits. Furthermore, a parameter sensitivity analysis is performed. The results are assessed considering a benchmark from the literature and showed a reduction in the number of transistors when compared to the baseline ESPRESSO.

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“…John Koza demonstrated the feasibility of the GP in many application areas. Since then, the number of research in this field has spread and increased rapidly, and the concept of GP was widely applied in plenty of applications, such as classification [7,8], control [9,10], dynamic processes [11,12], electrical circuit design [13,14], chemical engineering including polymer design [15,16], regression [17,18], and signal processing [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…Despite all these GP successes and applications, standard breeding operators can spoil promising solutions, and there are some risks that the optimal structure will be difficult to find. Therefore, there have been many attempts to modify GP operators with the purpose of maintaining promising individuals and reaching optimal solutions [13,22].…”

Section: Introductionmentioning

confidence: 99%

Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search

2022

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The foundation of machine learning is to enable computers to automatically solve certain problems. One of the main tools for achieving this goal is genetic programming (GP), which was developed from the genetic algorithm to expand its scope in machine learning. Although many studies have been conducted on GP, there are many questions about the disruption effect of the main GP breeding operators, i.e., crossover and mutation. Moreover, this method often suffers from high computational costs when implemented in some complex applications. This paper presents the meta-heuristics programming framework to create new practical machine learning tools alternative to the GP method. Furthermore, the immune system programming with local search (ISPLS) algorithm is composed from the proposed framework to enhance the classical artificial immune system algorithm with the tree data structure to deal with machine learning applications. The ISPLS method uses a set of breeding procedures over a tree space with gradual changes in order to surmount the defects of GP, especially the high disruptions of its basic operations. The efficiency of the proposed ISPLS method was proven through several numerical experiments, including promising results for symbolic regression, 6-bit multiplexer and 3-bit even-parity problems.

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Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design

Cited by 8 publications

References 30 publications

Towards a General Boolean Function Benchmark Suite

Towards a General Boolean Function Benchmark Suite

On the Use of Clonal Selection Principle in Cartesian Genetic Programming for Designing Combinational Logic Circuits

Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search

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