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Self-tuning metaheuristics have emerged as a significant advancement in the field of optimization algorithms in recent years, since fine tuning can be a very long and difficult process. [3] These algorithms differentiate themselves by their ability to autonomously adjust their parameters in response to the problem at hand, enhancing efficiency ...
A skew heap (or self-adjusting heap) is a heap data structure implemented as a binary tree. Skew heaps are advantageous because of their ability to merge more quickly than binary heaps. In contrast with binary heaps, there are no structural constraints, so there is no guarantee that the height of the tree is logarithmic. Only two conditions ...
Recursive self-improvement (RSI) is a process in which an early or weak artificial general intelligence (AGI) system enhances its own capabilities and intelligence without human intervention, leading to a superintelligence or intelligence explosion.
Examples include computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real-world problem. [48] Even a specific task like translation requires a machine to read and write in both languages, follow the author's argument (reason), understand the context (knowledge), and faithfully reproduce ...
The autonomous trait of self-optimization involves no human intervention at all during the aforementioned optimization process. In the area of control engineering most compact controllers for the industrial sector include an automatic adjustment of the control parameters to the connected section. This function is called auto-tuning or self ...
Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain. [1] [2] For example, as an aircraft flies, its mass will slowly decrease as a result of fuel consumption; a control law is needed that adapts itself to such changing conditions.
Another example of heuristic making an algorithm faster occurs in certain search problems. Initially, the heuristic tries every possibility at each step, like the full-space search algorithm. But it can stop the search at any time if the current possibility is already worse than the best solution already found.
For example, there are different ways to initialize the particles and velocities (e.g. start with zero velocities instead), how to dampen the velocity, only update p i and g after the entire swarm has been updated, etc. Some of these choices and their possible performance impact have been discussed in the literature.