Does the Field of Nature-Inspired Computing Contribute to Achieving Lifelike Features?

Area of Science:

• Computer Science
• Artificial Intelligence
• Computational Biology

Background:

• Artificial intelligence (AI) initially aimed to create practical computing systems with lifelike properties by studying living systems.
• Evolutionary computing emerged from this, using evolutionary principles for intelligent problem-solving.
• The success of evolutionary computing inspired a surge in nature-inspired algorithms, often claiming lifelike inspiration.

Purpose of the Study:

• To present nature-inspired methods that contribute to lifelike computing systems.
• To critically evaluate whether current nature-inspired algorithms maintain lifelike features.
• To discuss the necessity of new lifelike features in computing.

Main Methods:

• Review of evolutionary computing principles and their application.
• Analysis of nature-inspired algorithms and their claimed lifelike properties.
• Examination of the mathematical underpinnings versus claimed natural behaviors.

Main Results:

• Evolutionary computing effectively uses lifelike principles for problem-solving.
• Many recent nature-inspired algorithms lack rigorous mathematical grounding corresponding to their claimed natural inspirations.
• The direct contribution of some nature-inspired algorithms to achieving true lifelike computing is questionable.

Conclusions:

• Not all nature-inspired algorithms consistently exhibit or contribute to lifelike computing features.
• A critical assessment of the 'lifelike' claims and computational contributions of nature-inspired algorithms is necessary.
• Further research should focus on developing algorithms with demonstrable lifelike properties and genuine computational advancements.