Hybrid evolution method based on octopus learning and memory system bionics

Abstract

The invention discloses a bionic hybrid evolution method based on an octopus learning and memory system, which comprises the following steps: firstly, setting individual codes and an initialized population, and then constructing a learning and memory network: using two networks S and V, using the S as a sensing network for simulating a first fan-out synaptic layer, using the V network as a memory network corresponding to a fan-in synaptic layer of a vertical leaf structure, and using the memory network as a second fan-out synaptic layer of a vertical leaf structure for simulating a second fan-out synaptic layer; selecting individuals according to values in the learning memory network, and then carrying out individual crossing; finally, performing individual mutation operations according to the high and low mutation probability. According to the method, the learning memory network is introduced to select the individuals, the individuals which are determined to be the local optimal region are removed, the situation that the population individuals fall into the searched region can be effectively inhibited, the population evolution speed can be increased, and the population can effectively jump out of the local optimal solution.

Description

technical field [0001] The invention belongs to the technical field of automation, in particular to a bionic hybrid evolution method based on an octopus learning and memory system. Background technique [0002] The octopus is a wise man in the ocean, with a unique vertical leaf structure, which can process multi-modal sensory information and build memory tracks, can perceive and quickly learn the complex characteristics of its world, and solve problems in unique and innovative ways, but many Key details are still unknown, and research on the central nervous system is relatively lagging. The octopus uses the nervous system to perceive environmental feedback, make an evaluation, and then proceed to the next action. When the attack fails, it can learn quickly and memorize it for a long time to limit this behavior, so as to achieve the purpose of avoiding disadvantages and obtaining food efficiently. Through unique methods such as neural perception, gene editing and behavioral ...

AI Technical Summary

Problems solved by technology

[0007] The method of the invention is inspired by the complex learning and memory behavior function of the octopus vertical leaf system, which can inhibit the innate aggressive behavior, learn quickly and limit this behavior for a long time, so as to solve complex production scheduling, controller optimization design, robot path Technical problems in planning, robot inverse kinematics solution and image processing

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