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A Hybrid Evolutionary Algorithm for Evolving a Conscious Machine

  • Vijay A. KanadeEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

The paper discloses a novel concept of developing a conscious machine. Human consciousness is a driving factor behind the presented concept. ‘Integrated Information Theory (IIT)’ is applied to the hardware circuits in order to make the circuit(s) with a certain configuration active/alive. We have used an evolutionary algorithm that combines ‘Evolvable Hardware’ with ‘Integrated Information Theory of Consciousness’ to develop a conscious set of machines. Evolvable hardware is simulated by using Darwin’s evolution theory that is related to Genetic Algorithms (GA). Further, IIT is integrated into the results of first GA so as to harness the consciousness factor in circuits with a certain circuit configuration. The results of the evolutionary algorithm are evaluated to validate the proposed concept.

Keywords

Consciousness Integrated Information Theory (IIT) Hybrid evolutionary algorithm Field Programmable Gate Array (FPGA) Application Specific Integrated Circuit (ASIC) 

Notes

Acknowledgement

I would like to extend my sincere gratitude to Dr. A. S. Kanade for his relentless support during my research work.

References

  1. 1.
    Torresen, J.: An evolvable hardware (2004)Google Scholar
  2. 2.
    Joglekar, A., Tungare, M.: Gentic algorithms and their use in the design of evolvable hardware, 3 April 2000Google Scholar
  3. 3.
    Sekanina, L.: Evolvable hardware: from applications to implications for the theory of computation (2009)Google Scholar
  4. 4.
    Tononi, G., Sporns, O.: Measuring information integration, 02 December 2003Google Scholar
  5. 5.
    Kim, H., Hudetz, A.G., Lee, J., Mashour, G.A., Lee, U., ReCCognition Study Group: Estimating the integrated information measure phi from high-density electroencephalography during states of consciousness in humans, 16 February 2018Google Scholar
  6. 6.
    Vasicek, Z.: Bridging the gap between evolvable hardware and industry using cartesian genetic programming. In: Stepney, S., Adamatzky, A. (eds.) Inspired by Nature. Emergence, Complexity and Computation, vol. 28. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-67997-6_2CrossRefGoogle Scholar
  7. 7.
    Sekanina, L.: Evolutionary hardware design (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Intellectual Property and Research and DevelopmentEvalueserve (SEZ) Pvt. Ltd.New Delhi (NCR)India

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